diff --git a/.github/workflows/unittest.yml b/.github/workflows/unittest.yml
index 0f92dd7..80caf00 100644
--- a/.github/workflows/unittest.yml
+++ b/.github/workflows/unittest.yml
@@ -29,7 +29,7 @@ jobs:
       - name: run unit tests
         run: |
           echo "running unit tests"
-          python -m pytest --cov=src --cov-report=xml --cov-report=term-missing tests/
+          python -m pytest --cov=src --cov-report=xml --cov-report=term-missing -m "not cuda_required"
       - name: upload coverage to codecov
         uses: codecov/codecov-action@v4
         with:
diff --git a/.gitignore b/.gitignore
index 68bc17f..8dc3858 100644
--- a/.gitignore
+++ b/.gitignore
@@ -158,3 +158,5 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+.envrc
+src/bm3dornl/_version.py
diff --git a/environment.yml b/environment.yml
index 318ba37..19ccf62 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,39 +1,38 @@
 name: bm3dornl
 channels:
   - conda-forge
+  - nvidia
 dependencies:
-  # -- Runtime dependencies
-  # base: list all base dependencies here
-  - python>=3.8  # please specify the mimimum version of python here
+  # base
+  - python>=3.10
+  - pip
   - versioningit
-  # compute: list all compute dependencies here
-  - numpy
-  - pandas
-  # plot: list all plot dependencies here, if applicable
+  # compute
+  - cupy
+  - numba
+  - scipy<1.13  # avoid a bug in 1.13
+  - scikit-image
+  # [Optional]visualization
   - matplotlib
-  # jupyter: list all jupyter dependencies here, if applicable
+  # [Optional]jupyter
   - jupyterlab
-  - ipympl
-  # -- Development dependencies
-  # utils:
+  # utils
   - pre-commit
-  # pacakge building:
-  - libmamba
-  - libarchive
+  - line_profiler  # useful for development
+  - memory_profiler  # useful for development
+  # packaging
   - anaconda-client
   - boa
-  - conda-build < 4  # conda-build 24.x has a bug, missing update_index from conda_build.index
+  - conda-build < 4
   - conda-verify
+  - libmamba
+  - libarchive
   - python-build
-  # test: list all test dependencies here
+  # test
   - pytest
   - pytest-cov
+  - pytest-mock
   - pytest-xdist
-  # --------------------------------------------------
-  # add additional sections such as Qt, etc. if needed
-  # --------------------------------------------------
-  # if pakcages are not available on conda, list them here
-  - pip
+  # pip packages
   - pip:
-    - bm3d-streak-removal # example
-    - pytest-playwright
+    - bm3d-streak-removal  # this is our reference package
diff --git a/notebooks/example.ipynb b/notebooks/example.ipynb
index 7fa8701..ef4fb06 100644
--- a/notebooks/example.ipynb
+++ b/notebooks/example.ipynb
@@ -6,20 +6,408 @@
    "source": [
     "# Overview\n",
     "\n",
-    "This folder is used to store notebooks that demonstrate how to use the library in an interactive environment like Jupyter."
+    "This notebook provides an example usage for using bm3dornl to remove streaks from single sinogram."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "when on a multi-gpu system, make sure specify the card you are using to avoid affecting other people's job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Set the GPU device ID to 0 for this notebook session\n",
+    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare synthetic noisy sinogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bm3dornl.phantom import (\n",
+    "    shepp_logan_phantom,\n",
+    "    generate_sinogram,\n",
+    "    simulate_detector_gain_error,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define image size\n",
+    "image_size = 512  # smaller size runs faster on local machine, large number means wider image\n",
+    "scan_step = 0.5  # deg, smaller number means taller image\n",
+    "detector_gain_range=(0.98, 1.02)  # variation along detector width\n",
+    "detector_gain_error=0.01  # variation along time/rotation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3.16 s, sys: 21.4 ms, total: 3.18 s\n",
+      "Wall time: 3.22 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "# make shepp_logan 2D phantom\n",
+    "shepp_logan_2d = shepp_logan_phantom(\n",
+    "    size=image_size,\n",
+    "    contrast_factor=8,\n",
+    "    )\n",
+    "\n",
+    "# transform to sinogram\n",
+    "sino_org, thetas_deg = generate_sinogram(\n",
+    "    input_img=shepp_logan_2d,\n",
+    "    scan_step=scan_step,\n",
+    "    )\n",
+    "\n",
+    "# add detector gain error\n",
+    "sino_noisy, detector_gain = simulate_detector_gain_error(\n",
+    "    sinogram=sino_org,\n",
+    "    detector_gain_range=detector_gain_range,\n",
+    "    detector_gain_error=detector_gain_error,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(512, 512) (720, 512) (720, 512)\n",
+      "1e-08 1.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "ax[0].imshow(shepp_logan_2d, cmap='gray')\n",
+    "ax[0].set_title('Original Shepp-Logan Phantom')\n",
+    "ax[1].imshow(sino_org, cmap='gray')\n",
+    "ax[1].set_title('Sinogram')\n",
+    "ax[2].imshow(sino_noisy, cmap='gray')\n",
+    "ax[2].set_title('Sinogram with Detector Gain Error')\n",
+    "\n",
+    "print(shepp_logan_2d.shape, sino_org.shape, sino_noisy.shape)\n",
+    "print(sino_noisy.min(), sino_noisy.max())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "estimate background"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zcfbsWUUbIQSKi4vhcDgQHx+PSZMm4fTp04o2Ho8HS5YsQWpqKhISEjBr1ixcuHBBfW969XmIXpeIwucLrQPoyRKEw0D8SiY1NTVYvHgxPvjgA1RXV6OjowO5ubn45ptv5Dbr1q3D+vXrsXnzZhw9ehR2ux1Tp05Fa2ur3CY/Px/79u1DRUUFDh06hMuXL2PGjBnw+UJx67GmELwmEYXXV1oHQAOQhBAi0Cf/7//+L2688UbU1NTgRz/6EYQQcDgcyM/Px1NPPQXgahVis9nw29/+Fo899hjcbjduuOEGvPbaa5g3bx4A4G9/+xvS0tJQWVmJadOmDfi+LS0tsFqtAFYBiB2wvRDFgXaRiCKAJBUPsqUHwFq43W4kJSWFJJauzx/3LiBpqIrX+Raw/hwhjTWcVE3Au91uAEBycjIAoL6+Hi6XC7m5uXKb2NhYTJw4EYcPHwYA1NbWor29XdHG4XAgMzNTbtOdx+NBS0uL4iAi0hSHuRQCTiZCCBQUFOCee+5BZmYmAMDlunplkc2mnPC22WzyYy6XCzExMRg2bFifbborLS2F1WqVj7S0tEDDJiKiEAg4mTzxxBP46KOP8Prrr/d4TJKUN7IRQvQ4111/bQoLC+F2u+WjoaHBr1gHeGsiimCSVK91CL1jZaIQUDJZsmQJ9u/fj/feew8333ztRgN2ux0AelQYTU1NcrVit9vh9XrR3NzcZ5vuYmNjkZSUpDj8U+1neyKKHDVaB9C7rl2DAz0MdpWfX90RQuCJJ57AG2+8gXfffRfp6emKx9PT02G321Fdfe3D2+v1oqamBjk5OQAAp9OJ6OhoRZvGxkacOnVKbhN8J0P0ukQUehG4LJh68KvQWrx4MXbv3o3//M//RGJiolyBWK1WxMfHQ5Ik5Ofno6SkBBkZGcjIyEBJSQmGDh2K+fPny20XLlyI5cuXIyUlBcnJyVixYgXGjh2LKVOmBL+HAABO2BPpVwRupQJwO5Vu/OpOWVkZAGDSpEmK86+88goeeeQRAMDKlSvR1taGRYsWobm5GdnZ2aiqqkJiYqLcfsOGDbBYLJg7dy7a2towefJk7NixA1FRBrsklIiMi8lEQdV1Jlrx9zqTqx6CEBkhjIqIgk2S9sK/YeowXmfyDpCUoOJ1vgGs03idiQ69pXUAROS3SNx7nnpjsEKrP9yOgUh/InS+BOAwVzcG6w4RGYUkRfjtI7hrsIKJhrkASdqvdQhENGh7tA6A/GCqZMLrTYj0JMKHpnkFvILBujOQCB5/JSJ94T3gFUxWmQCSxK1ViCKdJJVpHQL5yXTJBPhQ6wCIaEARPvkOcJirG4N1ZzA41EUUySQpVLfwDjKu5lIwYWUCSBJXiRBFrv9H6wAoACasTADgjNYBEFGfdLIxKyfgFUyaTIiIVOIV8AqmHOYCAEn6rdYhEFE3klSsdQiDxwl4BdMmE6BN6wCIiAzDYLmRiPRKd6MFHOZSMHFlorOSmsjw9DVaIIYAIkrFYbBPX4N1h4j0iFe865/pk4kkPad1CESkhyveu/FZ1B9GYrDuBIJXxBOR/9QmBKMlE9NXJoAOJ/6IDIRzl8ZgsNwYKH1N/BGR9jqiJHRESSqeLwCI4AWkMSaTv5Ok9RCiQOswiExFz1WJz2KBzxJ4MvFZBIw0zM5hLplO9gMiIopArEyuw+qEKHz0XJUAgC8qCj4Vw1y+KGNVJkwmCqxOiGhwOhEFHwJPJp0Gmi8BOMzVA1d2EYWe3qsSAOhAlOrDSJhMeuDKLiIifzGZ9IJXxROFjhGqEgDwIQo+WFQcxqpMOGfSK+NMihFRaFxNJoF/H1cz3xKJWJkQUdgYpSqhnphM+sBfeiLqz9XKRN1hJBzmIqKwMNoXNA5zKbEy6YfRfvmJiEKFyYSIQs6IX8x8Kq8xCXSYa8uWLUhPT0dcXBycTifef//9ftvX1NTA6XQiLi4Oo0ePxtatW3u0+frrr7F48WIMHz4ccXFxGDNmDCorK/2Ki8lkAEb8R0AUTpK0X+sQQkLdsuCrh7/27NmD/Px8FBUVoa6uDhMmTMD06dNx/vz5XtvX19fjvvvuw4QJE1BXV4fVq1fjySefxN69e+U2Xq8XU6dOxeeff44//OEPOHv2LLZt24abbrrJr9gkIYTurulvaWmB1WoFsApAbBjeMRpCFIXhfYiMJ7xfyDwA1sLtdiMpKSkk79D1+fNn9y34TlLgk+iXW3y42/r/+RVrdnY27rzzTpSVXbvN8ZgxYzB79myUlpb2aP/UU09h//79OHPmjHwuLy8Pf/nLX3DkyBEAwNatW/H888/jf/7nfxAdHR1wf1iZDAqvOyEKhFGrkmBqaWlRHB6Pp9d2Xq8XtbW1yM3NVZzPzc3F4cOHe33OkSNHerSfNm0ajh07hvb2q59r+/fvx/jx47F48WLYbDZkZmaipKQEPp/Pr34wmQwSr4onCsRxrQMImWAtDU5LS4PVapWP3ioMALh48SJ8Ph9sNpvivM1mg8vl6vU5Lper1/YdHR24ePEiAOCzzz7DH/7wB/h8PlRWVuLpp5/GCy+8gOee8+8zj0uDB43VCZE/JOktrUMIKbWbNXb8/b8NDQ2KYa7Y2P6H7iVJuaRYCNHj3EDtrz/f2dmJG2+8ES+99BKioqLgdDrxt7/9Dc8//zz+7d/+bbDdYTLxhyQ9x7kTokH7b60D0IWkpKRBzZmkpqYiKiqqRxXS1NTUo/roYrfbe21vsViQkpICABg+fDiio6MRFXUtMY4ZMwYulwterxcxMTGD6odfw1xlZWUYN26c3Pnx48fjrbeuffsQQqC4uBgOhwPx8fGYNGkSTp8+rXgNj8eDJUuWIDU1FQkJCZg1axYuXLjgTxgaYnVCNBiSVK11CCHXqXIlV6ef3+VjYmLgdDpRXa382VZXVyMnJ6fX54wfP75H+6qqKmRlZcmT7XfffTc+/fRTdHZ2ym3++te/Yvjw4YNOJICfyeTmm2/G2rVrcezYMRw7dgw//vGP8ZOf/EROGOvWrcP69euxefNmHD16FHa7HVOnTkVra6v8Gvn5+di3bx8qKipw6NAhXL58GTNmzPB7skcrvN8J0WD8WesAQk6L7VQKCgrw8ssvY/v27Thz5gyWLVuG8+fPIy8vDwBQWFiIX/ziF3L7vLw8nDt3DgUFBThz5gy2b9+O8vJyrFixQm7z+OOP49KlS1i6dCn++te/4o9//CNKSkqwePFiv2LzKzXOnDlT8efnnnsOZWVl+OCDD3Dbbbdh48aNKCoqwpw5cwAAO3fuhM1mw+7du/HYY4/B7XajvLwcr732GqZMmQIA2LVrF9LS0nDgwAFMmzbNr+C1wfudEPVHkmq1DsGw5s2bh0uXLmHNmjVobGxEZmYmKisrMXLkSABAY2Oj4pqT9PR0VFZWYtmyZXjxxRfhcDiwadMmPPDAA3KbtLQ0VFVVYdmyZRg3bhxuuukmLF26FE899ZRfsQV8nYnP58N//Md/YMGCBairq0NcXBxuueUWHD9+HHfccYfc7ic/+Qm++93vYufOnXj33XcxefJkfPXVVxg2bJjc5vbbb8fs2bPx7LPP9vpeHo9HsVyupaUFaWlpCN91Jt3FQwj/ftBEZqHthb7hu86k0u1EgorrTL5p8eE+a21IYw0nv5cGnzx5Et/5zncQGxuLvLw87Nu3D7fddps8ydPfsjWXy4WYmBhFIunepjelpaWKpXNXE4mWWJ0QmZ0PQ1QOcxnrygy/e/O9730PJ06cwAcffIDHH38cCxYswMcffyw/7u+ytcG0KSwshNvtlo+GhgZ/ww46SSobuBGRyfB6LPPye2lwTEwMbr31VgBAVlYWjh49it///vfy+JrL5cLw4cPl9tcvW7Pb7fB6vWhublZUJ01NTX2uRgCurrseaO11+H2pdQBEEcg8Kx7VX2eiu52s+qW6zhJCwOPxID09HXa7XbEMzev1oqamRk4UTqcT0dHRijaNjY04depUv8mEiCKfJK3XOoSw0mKjx0jmV29Wr16N6dOnIy0tDa2traioqMDBgwfx9ttvQ5Ik5Ofno6SkBBkZGcjIyEBJSQmGDh2K+fPnAwCsVisWLlyI5cuXIyUlBcnJyVixYgXGjh0rr+7SE0kqhhDFWodBFCFatA4grDpV3i2x02CViV/J5Msvv8TDDz+MxsZGWK1WjBs3Dm+//TamTp0KAFi5ciXa2tqwaNEiNDc3Izs7G1VVVUhMTJRfY8OGDbBYLJg7dy7a2towefJk7NixQ3H1JRER6Qu3oFeN29MTRc59f8K3NPh1948xNCnwoapvWzrwoPVdwywNNtagnSbMM+FIRNd0YIjKCfjOgRvpiLEWOmuEy4TJzLgcmABWJkHCZcJkZuasztWuyPKZeQKeiIiuCnSzxmvP5zAX9YK7CZMZRc7EO2mNlUnQcL8uIjNhZaLEZEJEFACfyu1UjJZMOMwVRGbbToLMjau46HqsTILKXNtJkNmZcxVXF67mUmIyISIKQNf9TAJ/vj5uVT5YHOYKMknaoXUIRCEnSZu0DkFzWtwDPpIxmQTdF1oHQBQGX2kdAEUYDnMFnbnHkYnMQv3SYGNVJkwmREQBUL802FjJhMNcRESkGpNJCHAXYTIyXk91FW/bq2Ss3kQMXm9CRsatgwDOmXTHyiQk+I+NjIyLTKgnViZERAFQf9Gisb7LM5kQEQWgQ+VqLjXPjUTGSo1ERKQJViZERAFQv9GjsfbmYjIhIgpAp8rVXJ0GG+ZiMiEiCgCXBitxzoSIiFRjZUJEFAAuDVZiMiEiCkAHohDFpcEyY6VGIiLSBCsTIqIAqF8abKyPX2P1hojCIBrcn4tLg7vjMFdIxGsdAFEI8febemIyCYkkrQMgChkhCrQOISJ0XWei5jASDnOFgBCPax0CEYVYB6IwhKu5ZKxMiIhINVYmREQBuDpUpWY1l7EqEyaToIvWOgCiMEgG8JXWQWiKe3MpcZgr6EZqHQBRyAnxpNYhaI4T8EpMJkEmxM+1DoGIKOw4zEVEFABetKjEZBJUvL6EzORWAJ9qHYRmOhAFiUuDZaqGuUpLSyFJEvLz8+VzQggUFxfD4XAgPj4ekyZNwunTpxXP83g8WLJkCVJTU5GQkIBZs2bhwoULakKJCLyYi8yEQ7p0vYCTydGjR/HSSy9h3LhxivPr1q3D+vXrsXnzZhw9ehR2ux1Tp05Fa2ur3CY/Px/79u1DRUUFDh06hMuXL2PGjBnw+Yx1T2QiMq6upcGBH6xMcPnyZTz00EPYtm0bhg0bJp8XQmDjxo0oKirCnDlzkJmZiZ07d+Lbb7/F7t27AQButxvl5eV44YUXMGXKFNxxxx3YtWsXTp48iQMHDgSnV5rgfkVkPkIUax2CZriaSymgZLJ48WLcf//9mDJliuJ8fX09XC4XcnNz5XOxsbGYOHEiDh8+DACora1Fe3u7oo3D4UBmZqbcpjuPx4OWlhbFEWmEeErrEIiINOP3BHxFRQWOHz+Oo0eP9njM5XIBAGw2m+K8zWbDuXPn5DYxMTGKiqarTdfzuystLcWzzz7rb6hEFBbm3JLep3JvLlNXJg0NDVi6dCl27dqFuLi4PttJkqT4sxCix7nu+mtTWFgIt9stHw0NDf6EHQa2gZsQGZQQRVqHoIkORKk+jMSvZFJbW4umpiY4nU5YLBZYLBbU1NRg06ZNsFgsckXSvcJoamqSH7Pb7fB6vWhubu6zTXexsbFISkpSHJGEuwQTkdn5lUwmT56MkydP4sSJE/KRlZWFhx56CCdOnMDo0aNht9tRXV0tP8fr9aKmpgY5OTkAAKfTiejoaEWbxsZGnDp1Sm6jL9yLi8iME/GdqlZyWdBpsMv8/OpNYmIiMjMzFecSEhKQkpIin8/Pz0dJSQkyMjKQkZGBkpISDB06FPPnzwcAWK1WLFy4EMuXL0dKSgqSk5OxYsUKjB07tseEvh6YtcQnMjufyosWjTZnEvTUuHLlSrS1tWHRokVobm5GdnY2qqqqkJiYKLfZsGEDLBYL5s6di7a2NkyePBk7duxAVJSxfrhE5pIEIPJWWoaKD0NUJhNjbY0oCSGE1kH4q6WlBVarFcAqALEaRjIGQszT8P2JIoskFWscgQfAWrjd7pDNrXZ9/tzj/gMsSQkBv05Hyzc4ZP3nkMYaTsYatAszJhKi7syzTPjqaizuzdXFWHVWWCVrHQBRxDHTHKK6rVQsqu7SGImYTALEmwMREV3DZBIQ7sNF1BezLBPu7GWvLX8O3s+EuA8XEf19aS+XBndhZeI3XqRINDD9XTOmF1u2bEF6ejri4uLgdDrx/vvv99u+pqYGTqcTcXFxGD16NLZu3dpn24qKCkiShNmzZ/sdF5OJn8w0wUgUKCHu0TqEkNNiC/o9e/YgPz8fRUVFqKurw4QJEzB9+nScP3++1/b19fW47777MGHCBNTV1WH16tV48sknsXfv3h5tz507hxUrVmDChAl+xwUwmfiJVQnR4Bm7OunAEJUbPfr/8bt+/XosXLgQv/zlLzFmzBhs3LgRaWlpKCsr67X91q1bMWLECGzcuBFjxozBL3/5Szz66KP43e9+p2jn8/nw0EMP4dlnn8Xo0aMD+nkwmfiBVQnR4JmhOgmG7vdq8ng8vbbzer2ora1V3AsKAHJzc/u8F9SRI0d6tJ82bRqOHTuG9vZr1wOtWbMGN9xwAxYuXBhwP5hMiCiEjFudBOs6k7S0NFitVvkoLS3t9f0uXrwIn8/X6/2i+roXlMvl6rV9R0cHLl68CAD485//jPLycmzbtk3Vz4OruQbJLMsdiYJJiHsgSXq+HXffgrWaq6GhQbGdSmxs/1tE+Xu/qN7ad51vbW3Fz3/+c2zbtg2pqal+xd8dkwkRhdgUAMZLKJ0qk0nXdSaDvUdTamoqoqKi+r1fVHd2u73X9haLBSkpKTh9+jQ+//xzzJw581pcnZ0AAIvFgrNnz+KWW24ZVH84zDUIrEqIAse5k+CIiYmB0+lU3AsKAKqrq/u8F9T48eN7tK+qqkJWVhaio6Px/e9/v8c9qmbNmoV7770XJ06cQFpa2qDjY2UyACYSIvWEKI6AHYWDq0PlPeADuQK+oKAADz/8MLKysjB+/Hi89NJLOH/+PPLy8gBcvcX5F198gVdffRUAkJeXh82bN6OgoAD/8i//giNHjqC8vByvv/46ACAuLq7HPaq++93vAkCP8wNhMiGisDBaQvEhCkLFR2ggyWTevHm4dOkS1qxZg8bGRmRmZqKyshIjR44EcPWutddfc5Keno7KykosW7YML774IhwOBzZt2oQHHngg4Lj7wvuZ9INVCVFwhT6ZhO9+Jg73cQxJShz4CX3obGnF36x3GuZ+JpwzIaKwMdIXNC2ugI9kHOYiIgrA1WGu8M6ZRDJWJn0w0jcoIqJQYzIhorAyyhc1X2eU6sNIOMzVC6P8shNFLv3fK97XEYXOjsATglDx3EjEyqQH7gxMFGrcNNV4WJl0w19yonDRd3Xi67BA6gj8I1SoeG4kMlZvVGNVQhQuQhTp+iJGX8cQSKqGuYw1MMRkch1WJUThFg+gTesgAuLriFKZTDhnYlCsSojCTYintA6BgoSVyd+xKiHSij7nTjo6oiC1szLpwsqEiDSl1y9ywmdBp4pD+Iz1XZ7JBLyuhEh7HGbWOyYTItKcLquTjij1h4EYq84KSLLWARCRHqlNCAZLJqavTIR4UusQiAgcbtY7k1cmHKclogD5JKBDUvd8AzF1MtHlOC2RoSUBaNE6iMHp+Puh5vkGYvphLiKKHEIUaB0CBcjElckorQMgol7p5CJGViYKpq1MhHhE6xCIqFf/pHUAg9MRhMNATFqZcOKdKFIJMRWS9GetwxhYB9QVUAZLJiatTO7WOgAi6leS1gGQn0yZTISYpHUIRNSvh7QOYGC+IBwGYsJhrnitAyCiAQhhgxTpl2FwAl7Br8qkuLgYkiQpDrvdLj8uhEBxcTEcDgfi4+MxadIknD59WvEaHo8HS5YsQWpqKhISEjBr1ixcuHAhOL0ZlOwwvhcRBY5bHemJ38NcP/jBD9DY2CgfJ0+elB9bt24d1q9fj82bN+Po0aOw2+2YOnUqWltb5Tb5+fnYt28fKioqcOjQIVy+fBkzZsyAzxeemo9DXER6EeFDXVzNpeD3MJfFYlFUI12EENi4cSOKioowZ84cAMDOnTths9mwe/duPPbYY3C73SgvL8drr72GKVOmAAB27dqFtLQ0HDhwANOmTVPZHSIyCiFSInuoi8NcCn5XJp988gkcDgfS09Pxs5/9DJ999hkAoL6+Hi6XC7m5uXLb2NhYTJw4EYcPHwYA1NbWor29XdHG4XAgMzNTbtMbj8eDlpYWxRGYMQE+j4i0wTlOvfArmWRnZ+PVV1/FO++8g23btsHlciEnJweXLl2Cy+UCANhsNsVzbDab/JjL5UJMTAyGDRvWZ5velJaWwmq1ykdaWpo/YcuEmBfQ84hIKxG8jN8HdUNcBlvN5VcymT59Oh544AGMHTsWU6ZMwR//+EcAV4ezukjd6lIhRI9z3Q3UprCwEG63Wz4aGhr8CZuIdEqIe7QOoW+cM1FQdZ1JQkICxo4di08++USeR+leYTQ1NcnVit1uh9frRXNzc59tehMbG4ukpCTF4T9e9U5EFCqqkonH48GZM2cwfPhwpKenw263o7q6Wn7c6/WipqYGOTk5AACn04no6GhFm8bGRpw6dUpuEzpjQ/z6RBQaEbpEmJWJgl+ruVasWIGZM2dixIgRaGpqwm9+8xu0tLRgwYIFkCQJ+fn5KCkpQUZGBjIyMlBSUoKhQ4di/vz5AACr1YqFCxdi+fLlSElJQXJyMlasWCEPm4XWrBC/PhGFxiQAb2gdRE/tULc3lw42RvaHX8nkwoULePDBB3Hx4kXccMMN+OEPf4gPPvgAI0eOBACsXLkSbW1tWLRoEZqbm5GdnY2qqiokJibKr7FhwwZYLBbMnTsXbW1tmDx5Mnbs2IGoqNDeD1mIkL48EYWIEOMgSRGYTNRuiWKwCXhJCP19zLa0tMBqtQJYBSB2UM/h/aWJ9EuSigfZ0gNgLdxud4BzqwOTP3+2uYGhKt7j2xbgX6whjTWcTLI3l/7/oogownQtDVbzfAMxSTIZqXUARKSKDcCXWgehxCvgFUyyBT03dyTSN67GjHTmSCb2m7WOgIhUicCLF7k0WMEUyUQ0ah0BEakyQ+sAesFkomCKZEJE+ib+X60joIGYZAKeiCjIuJpLgcmEiCgQXM2lYIJhLm7wSEQUaiaoTEzQRSJTiEZEbWjVDkDNLlAR1JVg4CctEemEBRH1Ccy9uRSYTIhIJyJskoFzJgommDNp0zoAIgqKCKpKqAcTVCacgCcyhgibM+HSYAUTJBMiMoYImzPpgLoJeA5zERERKbEyISKdiLCv8u1Q93U8goqsYDBBMjHY3xiRaUXYv2UuDVbgMBcREalmgsqEiCgEuJpLwRSVifQvWkdARIbD+5komCKZ4E2tAyAiNaT/o3UENBBzDHNd/ARAhtZREFGg3tQ6gF60A5BUPt9AzJFMcBxMJkR69t9aB9ATV3MpmCSZfK51AESkykdaB9BTB9RNFHDORI+42SORvjVpHQANwCSVCRHpWwROMHBpsIJJKhNAkoTWIRCRkbQH4QjAli1bkJ6ejri4ODidTrz//vv9tq+pqYHT6URcXBxGjx6NrVu3Kh7ftm0bJkyYgGHDhmHYsGGYMmUKPvzwQ7/jMk0yAd7QOgAiCoAkfaJ1CBFjz549yM/PR1FREerq6jBhwgRMnz4d58+f77V9fX097rvvPkyYMAF1dXVYvXo1nnzySezdu1duc/DgQTz44IN47733cOTIEYwYMQK5ubn44osv/IpNEkLo7it7S0sLrFYrgFUAYgf5rGgIURTCqIgoFCSpDMCXg2ztAbAWbrcbSUlJIYlH/vyZ4QaiVbxHewvwX1a/Ys3Ozsadd96JsrIy+dyYMWMwe/ZslJaW9mj/1FNPYf/+/Thz5ox8Li8vD3/5y19w5MiRXt/D5/Nh2LBh2Lx5M37xi18MujsmqkwicMyViAZhsIkkzIJ0BXxLS4vi8Hg8vb6d1+tFbW0tcnNzFedzc3Nx+PDhXp9z5MiRHu2nTZuGY8eOob2998/Eb7/9Fu3t7UhOTh7gB6BkomRCRBR50tLSYLVa5aO3CgMALl68CJ/PB5vNpjhvs9ngcrl6fY7L5eq1fUdHBy5evNjrc1atWoWbbroJU6ZM8asfplrNJUk7IMQjWodBRIMkSRF4fUmXDqi7Av7vlUlDQ4NimCs2tv+he0lSvqkQose5gdr3dh4A1q1bh9dffx0HDx5EXFxcv3F0Z6pkwosXifTmgNYB9E3tRYd/f35SUtKg5kxSU1MRFRXVowppamrqUX10sdvtvba3WCxISUlRnP/d736HkpISHDhwAOPGjfOjI1dxmIuIIliL1gFEjJiYGDidTlRXVyvOV1dXIycnp9fnjB8/vkf7qqoqZGVlITo6Wj73/PPP49e//jXefvttZGVlBRSf6ZKJJL2ldQhEZAS+IBx+KigowMsvv4zt27fjzJkzWLZsGc6fP4+8vDwAQGFhoWIFVl5eHs6dO4eCggKcOXMG27dvR3l5OVasWCG3WbduHZ5++mls374do0aNgsvlgsvlwuXLl/2KzWTDXMDVTR+nax0EEQ1AkrZpHUL/gjTM5Y958+bh0qVLWLNmDRobG5GZmYnKykqMHDkSANDY2Ki45iQ9PR2VlZVYtmwZXnzxRTgcDmzatAkPPPCA3GbLli3wer3453/+Z8V7PfPMMyguLh50bCa6zuQaIYqDHRIRBZkkFQfwrDBeZ3KXG7CoeI+OFuCof9eZRDLTDXMBgCTt1zoEIuqHJPG6ML0xZTIBTmodABH1q2zgJlrrgLp9ucy+Bf0XX3yBn//850hJScHQoUPxj//4j6itrZUfF0KguLgYDocD8fHxmDRpEk6fPq14DY/HgyVLliA1NRUJCQmYNWsWLly4oL43g8ZvPUSR7SutAxiYBhPwkcyvZNLc3Iy7774b0dHReOutt/Dxxx/jhRdewHe/+125zbp167B+/Xps3rwZR48ehd1ux9SpU9Ha2iq3yc/Px759+1BRUYFDhw7h8uXLmDFjBny+8P10r+73Q0REweDXaq7f/va3SEtLwyuvvCKfGzVqlPz/Qghs3LgRRUVFmDNnDgBg586dsNls2L17Nx577DG43W6Ul5fjtddeky/X37VrF9LS0nDgwAFMmzYtCN0ajAjd74fI5CTpOa1DGJwOAGqWL5m5Mtm/fz+ysrLw05/+FDfeeCPuuOMObNt2bflefX09XC6XYmOx2NhYTJw4Ud6IrLa2Fu3t7Yo2DocDmZmZfW5W5vF4emyGRkRGpZNh6CBt9GgUfiWTzz77DGVlZcjIyMA777yDvLw8PPnkk3j11VcBQL5sv7+NyFwuF2JiYjBs2LA+23RXWlqq2AgtLS3Nn7D7pJtvQEQmweFn/fJrmKuzsxNZWVkoKSkBANxxxx04ffo0ysrKFFdd+rsR2UBtCgsLUVBQIP+5paUlSAlFJ9+AiExDR8PPHQA6VTxfzXMjkF+VyfDhw3Hbbbcpzo0ZM0a+4tJutwNAvxuR2e12eL1eNDc399mmu9jYWHkztMFuijZYkrQ+aK9FRCbC1VwKfiWTu+++G2fPnlWc++tf/ypfyp+eng673a7YWMzr9aKmpkbeiMzpdCI6OlrRprGxEadOnepzs7LQ4vwLUSQI7Ip3ihR+DXMtW7YMOTk5KCkpwdy5c/Hhhx/ipZdewksvvQTg6vBWfn4+SkpKkJGRgYyMDJSUlGDo0KGYP38+AMBqtWLhwoVYvnw5UlJSkJycjBUrVmDs2LF+34yFiEgzHVB32bfBhrn8SiZ33XUX9u3bh8LCQqxZswbp6enYuHEjHnroIbnNypUr0dbWhkWLFqG5uRnZ2dmoqqpCYmKi3GbDhg2wWCyYO3cu2traMHnyZOzYsQNRUVHB65kfJKmY+3URaUiXi2GYTBRMudFjb5hMiLQTvCGuMG70mOwGhqh4j84W4Ctu9Gg4HK8l0gYXwRiDCe9nQkSRRaeLYDqh7gp43Y0J9Y+VyXVYnRCFl66rEl4Br8BkQkQa0mlVQj0wmXTD6oQoPHRdlQCsTLrhnAkRaUTnVUk7gP53ieof50yMj9UJUWhJ0m+1DoGCjJUJEWmgTesA1POBlcl1WJn0gdUJUWjo8mr3vggVh8EwmRBRmPHWD0bEZNIPVidERIPDZEJEYcMvaMbFZDIA/vITEQ2MyWQQJGm/1iEQ6R6/mBkbk8mgHNc6ACKKOO1BOIyDyWSQWJ0QBc6YVQn3U7kek8mgsTohCoShriuhPjGZ+IHVCVEgjDWccw2Hua7HZOIXVidE/jB2VcJhrusxmfhJkt7SOgQiHTHWt2/qG5OJ3/5b6wCIdMHYVQlwtbJQM8RlrMqEuwYHQJIOQohJWodBFOGMXpWonfcw1s+HlUlADmodAFFE4/1KzIeVCRGFgAHuVzIgtZPoxhrmYmUSIGNehEWkniRt0jqEMOGcyfVYmRBRkH2ldQBhwsrkeqxMVOC4MJGSJO3QOgTSCCsTVcwwLkzkj8+1DiCMuJrreqxMVDLP+DARKfEK+OsxmahmlvFhov5xUYq5cZiLiCggXau51DzfOFiZBAG/kZHZmXMxCoe5rsdkQkRBwMUoZsdkEiSStF7rEIgorHg/k+txziRoWrQOgEgT5h3m5UWL12NlQkREqjGZBJE5JyHJzMx9xTv35roeh7mCipOQZDafax2AhjjMdT0mEyKigHA7letxmCvIuL0KEZmRX8lk1KhRkCSpx7F48WIAgBACxcXFcDgciI+Px6RJk3D69GnFa3g8HixZsgSpqalISEjArFmzcOHCheD1SHPcXoXMwfj3eB8IlwZfz69kcvToUTQ2NspHdXU1AOCnP/0pAGDdunVYv349Nm/ejKNHj8Jut2Pq1KlobW2VXyM/Px/79u1DRUUFDh06hMuXL2PGjBnw+XxB7BYRhZ6xPgz9xyvgr+dXMrnhhhtgt9vl47/+679wyy23YOLEiRBCYOPGjSgqKsKcOXOQmZmJnTt34ttvv8Xu3bsBAG63G+Xl5XjhhRcwZcoU3HHHHdi1axdOnjyJAwcOhKSDREQUegHPmXi9XuzatQuPPvooJElCfX09XC4XcnNz5TaxsbGYOHEiDh8+DACora1Fe3u7oo3D4UBmZqbcpjcejwctLS2KI5Jx3oSMTpL2aB1CBODS4OsFnEzefPNNfP3113jkkUcAAC6XCwBgs9kU7Ww2m/yYy+VCTEwMhg0b1meb3pSWlsJqtcpHWlpaoGGHSevATYh07QutA4gAHOa6XsDJpLy8HNOnT4fD4VCclyRJ8WchRI9z3Q3UprCwEG63Wz4aGhoCDTtMzD6WTMYX2aMDFH4BXWdy7tw5HDhwAG+88YZ8zm63A7hafQwfPlw+39TUJFcrdrsdXq8Xzc3NiuqkqakJOTk5fb5fbGwsYmNjAwmViChE2qHuUj1jfekMqDJ55ZVXcOONN+L++++Xz6Wnp8Nut8srvICr8yo1NTVyonA6nYiOjla0aWxsxKlTp/pNJkREkYfDXNfzO612dnbilVdewYIFC2CxXHu6JEnIz89HSUkJMjIykJGRgZKSEgwdOhTz588HAFitVixcuBDLly9HSkoKkpOTsWLFCowdOxZTpkwJXq+IiCis/E4mBw4cwPnz5/Hoo4/2eGzlypVoa2vDokWL0NzcjOzsbFRVVSExMVFus2HDBlgsFsydOxdtbW2YPHkyduzYgaioKHU9iTCSVAYhHtc6DCIKGd6293qSEEJoHYS/WlpaYLVaAawCEKlzKUkQokDrIIiCTpLKAHypdRh98ABYC7fbjaSkpJC8w7XPn2cBxKl4pSsAnglprOHEjR5DxliTa0TXcHfsq9oBqBlRMdZnBDd6DBljlbBE1xjrQ5CCQ5eVybWROY+mcfTPE/FX6hMF5jIi98vS1c+E8IzefwN1P4dI/vzyny6TybWNIzdoGsdArNa1WodAZEqtra1/n9cIvpiYGNjtdrhc6j9/7HY7YmJighCV9nQ5Ad/Z2YmzZ8/itttuQ0NDg+4nr1paWpCWlsa+RCAj9ccMfRFCoLW1FQ6HA0OGhG4U/8qVK/B6vapfJyYmBnFxaibxI4cuK5MhQ4bgpptuAgAkJSXp/h9GF/YlchmpP0bvS6gqkuvFxcUZJgkECyfgiYhINSYTIiJSTbfJJDY2Fs8884whNoBkXyKXkfrDvlAo6XICnoiIIotuKxMiIoocTCZERKQakwkREanGZEJERKoxmRARkWq6TCZbtmxBeno64uLi4HQ68f7772sdUg9/+tOfMHPmTDgcDkiShDfffFPxuBACxcXFcDgciI+Px6RJk3D69GlFG4/HgyVLliA1NRUJCQmYNWsWLly4EMZeXFVaWoq77roLiYmJuPHGGzF79mycPXtW0UYv/SkrK8O4cePkK6fHjx+Pt956S3f96E1paal8x9MueupPcXExJElSHHa7XX5cT30xJaEzFRUVIjo6Wmzbtk18/PHHYunSpSIhIUGcO3dO69AUKisrRVFRkdi7d68AIPbt26d4fO3atSIxMVHs3btXnDx5UsybN08MHz5ctLS0yG3y8vLETTfdJKqrq8Xx48fFvffeK26//XbR0dER1r5MmzZNvPLKK+LUqVPixIkT4v777xcjRowQly9f1l1/9u/fL/74xz+Ks2fPirNnz4rVq1eL6OhocerUKV31o7sPP/xQjBo1SowbN04sXbpUPq+n/jzzzDPiBz/4gWhsbJSPpqYmXfbFjHSXTP7pn/5J5OXlKc59//vfF6tWrdIoooF1TyadnZ3CbreLtWvXyueuXLkirFar2Lp1qxBCiK+//lpER0eLiooKuc0XX3whhgwZIt5+++2wxd6bpqYmAUDU1NQIIfTfn2HDhomXX35Zt/1obW0VGRkZorq6WkycOFFOJnrrzzPPPCNuv/32Xh/TW1/MSFfDXF6vF7W1tcjNzVWcz83NxeHDhzWKyn/19fVwuVyKfsTGxmLixIlyP2pra9He3q5o43A4kJmZqXlf3W43ACA5ORmAfvvj8/lQUVGBb775BuPHj9dtPxYvXoz7778fU6ZMUZzXY38++eQTOBwOpKen42c/+xk+++wzAPrsi9noatfgixcvwufzwWazKc7bbDa4XC6NovJfV6y99ePcuXNym5iYGAwbNqxHGy37KoRAQUEB7rnnHmRmZgLQX39OnjyJ8ePH48qVK/jOd76Dffv24bbbbpM/cPTSDwCoqKjA8ePHcfTo0R6P6e3vJTs7G6+++ir+4R/+AV9++SV+85vfICcnB6dPn9ZdX8xIV8mkiyRJij8LIXqc04NA+qF1X5944gl89NFHOHToUI/H9NKf733vezhx4gS+/vpr7N27FwsWLEBNTY38uF760dDQgKVLl6Kqqqrf7dD10p/p06fL/z927FiMHz8et9xyC3bu3Ikf/vCHAPTTFzPS1TBXamoqoqKienzLaGpq6vGNJZJ1rVDprx92ux1erxfNzc19tgm3JUuWYP/+/Xjvvfdw8803y+f11p+YmBjceuutyMrKQmlpKW6//Xb8/ve/110/amtr0dTUBKfTCYvFAovFgpqaGmzatAkWi0WORy/96S4hIQFjx47FJ598oru/GzPSVTKJiYmB0+lEdXW14nx1dTVycnI0isp/6enpsNvtin54vV7U1NTI/XA6nYiOjla0aWxsxKlTp8LeVyEEnnjiCbzxxht49913kZ6ernhcb/3pTggBj8eju35MnjwZJ0+exIkTJ+QjKysLDz30EE6cOIHRo0frqj/deTwenDlzBsOHD9fd340paTHrr0bX0uDy8nLx8ccfi/z8fJGQkCA+//xzrUNTaG1tFXV1daKurk4AEOvXrxd1dXXyEua1a9cKq9Uq3njjDXHy5Enx4IMP9rrM8eabbxYHDhwQx48fFz/+8Y81Web4+OOPC6vVKg4ePKhYtvntt9/KbfTSn8LCQvGnP/1J1NfXi48++kisXr1aDBkyRFRVVemqH325fjWXEPrqz/Lly8XBgwfFZ599Jj744AMxY8YMkZiYKP/b1lNfzEh3yUQIIV588UUxcuRIERMTI+688055iWokee+99wSAHseCBQuEEFeXOj7zzDPCbreL2NhY8aMf/UicPHlS8RptbW3iiSeeEMnJySI+Pl7MmDFDnD9/Pux96a0fAMQrr7wit9FLfx599FH5d+eGG24QkydPlhOJnvrRl+7JRE/96bpuJDo6WjgcDjFnzhxx+vRp+XE99cWMeD8TIiJSTVdzJkREFJmYTIiISDUmEyIiUo3JhIiIVGMyISIi1ZhMiIhINSYTIiJSjcmEiIhUYzIhIiLVmEyIiEg1JhMiIlLt/wdrGojKtXhBogAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bg_estimate = 1e-1\n",
+    "\n",
+    "bg = np.array(sino_noisy)\n",
+    "bg[sino_noisy >= bg_estimate] = np.nan\n",
+    "#\n",
+    "plt.imshow(bg, cmap=\"jet\")\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BM3D close source version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import bm3d_streak_removal as bm3dsr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Median filtering, iteration 0\n",
+      "Median filtering, iteration 1\n",
+      "Median filtering, iteration 2\n",
+      "CPU times: user 3.5 s, sys: 6.6 ms, total: 3.5 s\n",
+      "Wall time: 3.57 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "sion_bm3d_attenuated = bm3dsr.extreme_streak_attenuation(\n",
+    "    data=sino_noisy,\n",
+    "    extreme_streak_iterations=3,\n",
+    "    extreme_detect_lambda=4.0,\n",
+    "    extreme_detect_size=9,\n",
+    "    extreme_replace_size=2,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Denoising sinogram 0\n",
+      "k: 4\n",
+      "k: 3\n",
+      "k: 2\n",
+      "k: 1\n",
+      "k: 0\n",
+      "CPU times: user 5min 21s, sys: 2min 5s, total: 7min 26s\n",
+      "Wall time: 1min 49s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "sino_bm3d = bm3dsr.multiscale_streak_removal(\n",
+    "    data=sion_bm3d_attenuated,\n",
+    "    max_bin_iter_horizontal=4,\n",
+    "    bin_vertical=0,\n",
+    "    filter_strength=1.0,\n",
+    "    use_slices=True,\n",
+    "    slice_sizes=None,\n",
+    "    slice_step_sizes=None,\n",
+    "    denoise_indices=None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sion_bm3d_attenuated = sion_bm3d_attenuated[:, 0, :]\n",
+    "sino_bm3d = sino_bm3d[:, 0, :]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "axs[0].imshow(sino_noisy, cmap='gray')\n",
+    "axs[0].set_title('Noisy sinogram')\n",
+    "axs[1].imshow(sion_bm3d_attenuated, cmap='gray')\n",
+    "axs[1].set_title('BM3D extreme streak attenuation')\n",
+    "axs[2].imshow(sino_bm3d, cmap='gray')\n",
+    "axs[2].set_title('BM3D denoised sinogram')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BM3DRONL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bm3dornl.denoiser import bm3d_streak_removal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 42s, sys: 13.3 s, total: 1min 56s\n",
+      "Wall time: 1min 53s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "sino_bm3dornl = bm3d_streak_removal(\n",
+    "    sinogram=sino_noisy,\n",
+    "    background_threshold=0.1,\n",
+    "    patch_size=(8, 8),\n",
+    "    stride=3,\n",
+    "    cut_off_distance=(64, 64),\n",
+    "    intensity_diff_threshold=0.2,\n",
+    "    num_patches_per_group=512,\n",
+    "    shrinkage_threshold=0.1,\n",
+    "    k=4,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize noisy, org, sino_bm3d, and sino_bm3dornl\n",
+    "fig, axs = plt.subplots(1, 4, figsize=(16, 4))\n",
+    "axs[0].imshow(sino_noisy, cmap='gray')\n",
+    "axs[0].set_title('Noisy sinogram')\n",
+    "axs[0].axis('off')\n",
+    "axs[1].imshow(sino_org, cmap='gray')\n",
+    "axs[1].set_title('Original sinogram')\n",
+    "axs[1].axis('off')\n",
+    "axs[2].imshow(sino_bm3d, cmap='gray')\n",
+    "axs[2].set_title('BM3D denoised sinogram')\n",
+    "axs[2].axis('off')\n",
+    "axs[3].imshow(sino_bm3dornl, cmap='gray')\n",
+    "axs[3].set_title('BM3D ORNL denoised sinogram')\n",
+    "axs[3].axis('off')\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/pyproject.toml b/pyproject.toml
index 091cbb5..5c32e29 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -59,7 +59,7 @@ testpaths = ["tests"]
 python_files = ["test*.py"]
 norecursedirs = [".git", "tmp*", "_tmp*", "__pycache__", "*dataset*", "*data_set*"]
 markers = [
-    "mymarker: example markers goes here"
+    "cuda_required: test requires cuda to run."
 ]
 
 [tool.pylint]
diff --git a/src/bm3dornl/__init__.py b/src/bm3dornl/__init__.py
index 3ab36bc..6db246b 100644
--- a/src/bm3dornl/__init__.py
+++ b/src/bm3dornl/__init__.py
@@ -6,10 +6,3 @@
     from ._version import __version__  # noqa: F401
 except ImportError:
     __version__ = "0.0.1"
-
-
-def PackageName():  # pylint: disable=invalid-name
-    """This is needed for backward compatibility because mantid workbench does "from shiver import Shiver" """
-    from .packagenamepy import PackageName as packagename  # pylint: disable=import-outside-toplevel
-
-    return packagename()
diff --git a/src/bm3dornl/aggregation.py b/src/bm3dornl/aggregation.py
new file mode 100644
index 0000000..9738087
--- /dev/null
+++ b/src/bm3dornl/aggregation.py
@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+"""Functions for aggregating hyper patch block into a single image."""
+
+import numpy as np
+from numba import jit, prange
+
+
+@jit(nopython=True, parallel=True)
+def aggregate_patches(
+    estimate_denoised_image: np.ndarray,
+    weights: np.ndarray,
+    hyper_block: np.ndarray,
+    hyper_block_index: np.ndarray,
+):
+    """
+    Aggregate patches into the denoised image matrix and update the corresponding weights matrix.
+
+    Parameters
+    ----------
+    estimate_denoised_image : np.ndarray
+        The 2D numpy array where the aggregate result of the denoised patches will be stored.
+    weights : np.ndarray
+        The 2D numpy array that counts the contributions of the patches to the cells of the `estimate_denoised_image`.
+    hyper_block : np.ndarray
+        A 4D numpy array of patches to be aggregated. Shape is (num_blocks, num_patches_per_block, patch_height, patch_width).
+    hyper_block_index : np.ndarray
+        A 3D numpy array containing the top-left indices (row, column) for each patch in the `hyper_block`.
+        Shape is (num_blocks, num_patches_per_block, 2).
+
+    Notes
+    -----
+    This function uses Numba's JIT compilation with parallel execution to speed up the aggregation of image patches.
+    Each thread handles a block of patches independently, reducing computational time significantly on multi-core processors.
+    """
+    num_blocks, num_patches, ph, pw = hyper_block.shape
+    for i in prange(num_blocks):
+        for p in range(num_patches):
+            patch = hyper_block[i, p]
+            i_pos, j_pos = hyper_block_index[i, p]
+            for ii in range(ph):
+                for jj in range(pw):
+                    estimate_denoised_image[i_pos + ii, j_pos + jj] += patch[ii, jj]
+                    weights[i_pos + ii, j_pos + jj] += 1
diff --git a/src/bm3dornl/block_matching.py b/src/bm3dornl/block_matching.py
new file mode 100644
index 0000000..c46a516
--- /dev/null
+++ b/src/bm3dornl/block_matching.py
@@ -0,0 +1,162 @@
+#!/usr/bin/env python3
+"""Block matching to build hyper block from single sinogram."""
+
+import numpy as np
+from typing import Tuple, Optional
+from bm3dornl.utils import (
+    get_signal_patch_positions,
+    find_candidate_patch_ids,
+    is_within_threshold,
+    pad_patch_ids,
+)
+
+
+class PatchManager:
+    def __init__(
+        self,
+        image: np.ndarray,
+        patch_size: Tuple[int, int] = (8, 8),
+        stride: int = 1,
+        background_threshold: float = 0.1,
+    ):
+        """
+        Initialize the PatchManager with an image, patch configuration, and background threshold
+        for distinguishing between signal and background patches.
+
+        Parameters
+        ----------
+        image : np.ndarray
+            The image from which patches will be managed.
+        patch_size : tuple
+            Dimensions (height, width) of each patch. Default is (8, 8).
+        stride : int
+            The stride with which to slide the window across the image. Default is 1.
+        background_threshold : float
+            The mean intensity threshold below which a patch is considered a background patch.
+        """
+        self._image = image
+        self.patch_size = patch_size
+        self.stride = stride
+        self.background_threshold = background_threshold
+        self.signal_patches_pos = []
+        self.signal_blocks_matrix = []
+        self._generate_patch_positions()
+
+    def _generate_patch_positions(self):
+        """Generate the positions of signal and background patches in the image."""
+        self.signal_patches_pos = get_signal_patch_positions(
+            self._image, self.patch_size, self.stride, self.background_threshold
+        )
+
+    @property
+    def image(self):
+        return self._image
+
+    @image.setter
+    def image(self, value):
+        self._image = value
+        self._generate_patch_positions()
+
+    def get_patch(
+        self, position: tuple, source_image: Optional[np.ndarray] = None
+    ) -> np.ndarray:
+        """Retreive a patch from the image at the specified position.
+
+        Parameters:
+        ----------
+        position : tuple
+            The row and column indices of the top-left corner of the patch.
+        source_image : np.ndarray
+
+        Returns:
+        -------
+        np.ndarray
+            The patch extracted from the image.
+        """
+        source_image = self._image if source_image is None else source_image
+        i, j = position
+        return source_image[i : i + self.patch_size[0], j : j + self.patch_size[1]]
+
+    def group_signal_patches(
+        self, cut_off_distance: tuple, intensity_diff_threshold: float
+    ):
+        """
+        Group signal patches into blocks based on spatial and intensity distance thresholds.
+
+        Parameters:
+        ----------
+        cut_off_distance : tuple
+            Maximum spatial distance in terms of row and column indices for patches in the same block, Manhattan distance (taxi cab distance).
+        intensity_diff_threshold : float
+            Maximum Euclidean distance in intensity for patches to be considered similar.
+        """
+        num_patches = len(self.signal_patches_pos)
+        self.signal_blocks_matrix = np.eye(num_patches, dtype=bool)
+
+        # Cache patches as views
+        cached_patches = [self.get_patch(pos) for pos in self.signal_patches_pos]
+
+        for ref_patch_id in range(num_patches):
+            ref_patch = cached_patches[ref_patch_id]
+            candidate_patch_ids = find_candidate_patch_ids(
+                self.signal_patches_pos, ref_patch_id, cut_off_distance
+            )
+            # iterate over the candidate patches
+            for neightbor_patch_id in candidate_patch_ids:
+                if is_within_threshold(
+                    ref_patch,
+                    cached_patches[neightbor_patch_id],
+                    intensity_diff_threshold,
+                ):
+                    self.signal_blocks_matrix[ref_patch_id, neightbor_patch_id] = True
+                    self.signal_blocks_matrix[neightbor_patch_id, ref_patch_id] = True
+
+    def get_hyper_block(
+        self,
+        num_patches_per_group: int,
+        padding_mode="circular",
+        alternative_source: np.ndarray = None,
+    ):
+        """
+        Return groups of similar patches as 4D arrays with each group having a fixed number of patches.
+
+        Parameters:
+        ----------
+        num_patches_per_group : int
+            Number of patches in each group.
+        padding_mode : str
+            Mode for padding the patch IDs when the number of candidates is less than `num_patches_per_group`.
+            Options are 'first', 'repeat_sequence', 'circular', 'mirror', 'random'.
+        alternative_source : cp.ndarray
+            An alternative source image to extract patches from. Default is None.
+
+        Returns:
+        -------
+        tuple
+            A tuple containing the 4D array of patch groups and the corresponding positions.
+
+        TODO:
+        -----
+        - use multi-processing to further improve the speed of block building
+        """
+        group_size = len(self.signal_blocks_matrix)
+        block = np.empty(
+            (group_size, num_patches_per_group, *self.patch_size), dtype=np.float32
+        )
+        positions = np.empty((group_size, num_patches_per_group, 2), dtype=np.int32)
+
+        for i, row in enumerate(self.signal_blocks_matrix):
+            candidate_patch_ids = np.where(row)[0]
+            padded_patch_ids = pad_patch_ids(
+                candidate_patch_ids, num_patches_per_group, mode=padding_mode
+            )
+            # update block and positions
+            block[i] = np.array(
+                [
+                    self.get_patch(self.signal_patches_pos[idx], alternative_source)
+                    for idx in padded_patch_ids
+                ]
+            )
+            positions[i] = np.array(self.signal_patches_pos[padded_patch_ids])
+
+        return block, positions
diff --git a/src/bm3dornl/bm3dornl.py b/src/bm3dornl/bm3dornl.py
deleted file mode 100644
index d30dde6..0000000
--- a/src/bm3dornl/bm3dornl.py
+++ /dev/null
@@ -1,66 +0,0 @@
-"""
-Main Qt application
-"""
-
-import sys
-from qtpy.QtWidgets import QApplication, QMainWindow
-
-from mantid.kernel import Logger
-from mantidqt.gui_helper import set_matplotlib_backend
-
-# make sure matplotlib is correctly set before we import shiver
-set_matplotlib_backend()
-
-# make sure the algorithms have been loaded so they are available to the AlgorithmManager
-import mantid.simpleapi  # noqa: F401, E402 pylint: disable=unused-import, wrong-import-position
-
-from packagenamepy.configuration import Configuration  # noqa: E402 pylint: disable=wrong-import-position
-from packagenamepy.version import __version__  # noqa: E402 pylint: disable=wrong-import-position
-from packagenamepy.mainwindow import MainWindow  # noqa: E402 pylint: disable=wrong-import-position
-
-logger = Logger("PACKAGENAME")
-
-
-class PackageName(QMainWindow):
-    """Main Package window"""
-
-    __instance = None
-
-    def __new__(cls):
-        if PackageName.__instance is None:
-            PackageName.__instance = QMainWindow.__new__(cls)  # pylint: disable=no-value-for-parameter
-        return PackageName.__instance
-
-    def __init__(self, parent=None):
-        super().__init__(parent)
-        logger.information(f"PackageName version: {__version__}")
-        config = Configuration()
-
-        if not config.is_valid():
-            msg = (
-                "Error with configuration settings!",
-                f"Check and update your file: {config.config_file_path}",
-                "with the latest settings found here:",
-                f"{config.template_file_path} and start the application again.",
-            )
-
-            print(" ".join(msg))
-            sys.exit(-1)
-        self.setWindowTitle(f"PACKAGENAME - {__version__}")
-        self.main_window = MainWindow(self)
-        self.setCentralWidget(self.main_window)
-
-
-def gui():
-    """
-    Main entry point for Qt application
-    """
-    input_flags = sys.argv[1::]
-    if "--v" in input_flags or "--version" in input_flags:
-        print(__version__)
-        sys.exit()
-    else:
-        app = QApplication(sys.argv)
-        window = PackageName()
-        window.show()
-        sys.exit(app.exec_())
diff --git a/src/bm3dornl/configuration.py b/src/bm3dornl/configuration.py
deleted file mode 100644
index 86c68ba..0000000
--- a/src/bm3dornl/configuration.py
+++ /dev/null
@@ -1,107 +0,0 @@
-"""Module to load the the settings from SHOME/.packagename/configuration.ini file
-
-Will fall back to a default"""
-
-import os
-import shutil
-
-from configparser import ConfigParser
-from pathlib import Path
-from mantid.kernel import Logger
-
-logger = Logger("PACKAGENAME")
-
-# configuration settings file path
-CONFIG_PATH_FILE = os.path.join(Path.home(), ".packagename", "configuration.ini")
-
-
-class Configuration:
-    """Load and validate Configuration Data"""
-
-    def __init__(self):
-        """initialization of configuration mechanism"""
-        # capture the current state
-        self.valid = False
-
-        # locate the template configuration file
-        project_directory = Path(__file__).resolve().parent
-        self.template_file_path = os.path.join(
-            project_directory, "configuration_template.ini"
-        )
-
-        # retrieve the file path of the file
-        self.config_file_path = CONFIG_PATH_FILE
-        logger.information(f"{self.config_file_path} will be used")
-
-        # if template conf file path exists
-        if os.path.exists(self.template_file_path):
-            # file does not exist create it from template
-            if not os.path.exists(self.config_file_path):
-                # if directory structure does not exist create it
-                if not os.path.exists(os.path.dirname(self.config_file_path)):
-                    os.makedirs(os.path.dirname(self.config_file_path))
-                shutil.copy2(self.template_file_path, self.config_file_path)
-
-            self.config = ConfigParser(allow_no_value=True, comment_prefixes="/")
-            # parse the file
-            try:
-                self.config.read(self.config_file_path)
-                # validate the file has the all the latest variables
-                self.validate()
-            except ValueError as err:
-                logger.error(str(err))
-                logger.error(f"Problem with the file: {self.config_file_path}")
-        else:
-            logger.error(
-                f"Template configuration file: {self.template_file_path} is missing!"
-            )
-
-    def validate(self):
-        """validates that the fields exist at the config_file_path and writes any missing fields/data
-        using the template configuration file: configuration_template.ini as a guide"""
-        template_config = ConfigParser(allow_no_value=True, comment_prefixes="/")
-        template_config.read(self.template_file_path)
-        for section in template_config.sections():
-            # if section is missing
-            if section not in self.config.sections():
-                # copy the whole section
-                self.config.add_section(section)
-
-            for item in template_config.items(section):
-                field, _ = item
-                if field not in self.config[section]:
-                    # copy the field
-                    self.config[section][field] = template_config[section][field]
-        with open(self.config_file_path, "w", encoding="utf8") as config_file:
-            self.config.write(config_file)
-        self.valid = True
-
-    def is_valid(self):
-        """returns the configuration state"""
-        return self.valid
-
-
-def get_data(section, name=None):
-    """retrieves the configuration data for a variable with name"""
-    # default file path location
-    config_file_path = CONFIG_PATH_FILE
-    if os.path.exists(config_file_path):
-        config = ConfigParser()
-        # parse the file
-        config.read(config_file_path)
-        try:
-            if name:
-                value = config[section][name]
-                # in case of boolean string value cast it to bool
-                if value in ("True", "False"):
-                    return value == "True"
-                # in case of None
-                if value == "None":
-                    return None
-                return value
-            return config[section]
-        except KeyError as err:
-            # requested section/field do not exist
-            logger.error(str(err))
-            return None
-    return None
diff --git a/src/bm3dornl/configuration_template.ini b/src/bm3dornl/configuration_template.ini
deleted file mode 100644
index c8ea100..0000000
--- a/src/bm3dornl/configuration_template.ini
+++ /dev/null
@@ -1,2 +0,0 @@
-[global.other]
-help_url = https://github.com/neutrons/python_project_template/blob/main/README.md
diff --git a/src/bm3dornl/denoiser.py b/src/bm3dornl/denoiser.py
new file mode 100644
index 0000000..3347c79
--- /dev/null
+++ b/src/bm3dornl/denoiser.py
@@ -0,0 +1,301 @@
+#!/usr/bin/env python
+"""Denoiser module for BM3D-ORNL."""
+
+import logging
+import numpy as np
+from typing import Tuple
+from scipy.signal import medfilt2d
+from bm3dornl.aggregation import aggregate_patches
+from bm3dornl.block_matching import PatchManager
+from bm3dornl.gpu_utils import (
+    hard_thresholding,
+    wiener_hadamard,
+    memory_cleanup,
+)
+from bm3dornl.utils import (
+    horizontal_binning,
+    horizontal_debinning,
+)
+
+
+class BM3D:
+    def __init__(
+        self,
+        image: np.ndarray,
+        patch_size: Tuple[int, int] = (8, 8),
+        stride: int = 3,
+        background_threshold: float = 0.1,
+    ):
+        """
+        Initialize the BM3D class with an image and configuration parameters for patch management and denoising.
+
+        Parameters
+        ----------
+        image : np.ndarray
+            The sinogram or image to be denoised.
+        patch_size : tuple
+            Dimensions (height, width) of each patch. Default is (8, 8).
+        stride : int
+            The stride with which to slide the window across the image. Default is 3.
+        background_threshold : float
+            The mean intensity threshold below which a patch is considered a background patch.
+        """
+        self.image = np.asarray(image)
+        self.estimate_denoised_image = np.zeros_like(self.image, dtype=float)
+        self.final_denoised_image = np.zeros_like(self.image, dtype=float)
+        # Initialize the PatchManager
+        self.patch_manager = PatchManager(
+            self.image,
+            patch_size=patch_size,
+            stride=stride,
+            background_threshold=background_threshold,
+        )
+        # record input parameters
+        self.patch_size = patch_size
+        self.stride = stride
+        self.background_threshold = background_threshold
+
+    def group_signal_patches(
+        self, cut_off_distance: Tuple[int, int], intensity_diff_threshold: float
+    ):
+        """
+        Group signal patches into blocks based on spatial and intensity distance thresholds.
+
+        Parameters:
+        ----------
+        cut_off_distance : tuple
+            Maximum spatial distance in terms of row and column indices for patches in the same block.
+        intensity_diff_threshold : float
+            Maximum Euclidean distance in intensity for patches to be considered similar.
+        """
+        self.patch_manager.group_signal_patches(
+            cut_off_distance, intensity_diff_threshold
+        )
+        logging.info(
+            f"Total number of signal patches: {len(self.patch_manager.signal_patches_pos)}"
+        )
+
+    def thresholding(
+        self,
+        cut_off_distance: Tuple[int, int],
+        intensity_diff_threshold: float,
+        num_patches_per_group: int,
+        threshold: float,
+    ) -> np.ndarray:
+        """
+        Perform the denoising process using the specified configuration.
+
+        Parameters:
+        ----------
+        cut_off_distance : tuple
+            Maximum spatial distance in terms of row and column indices for patches in the same block.
+        intensity_diff_threshold : float
+            Maximum Euclidean distance in intensity for patches to be considered similar.
+        num_patches_per_group : int
+            The number of patchs in each block.
+        threshold : float
+            The threshold value for hard thresholding during the first pass.
+
+        Returns:
+        -------
+        np.ndarray
+            The denoised image estimate.
+        """
+        self.group_signal_patches(cut_off_distance, intensity_diff_threshold)
+
+        weights = np.zeros_like(self.image, dtype=float)
+
+        logging.info("Block matching for 1st pass...")
+        block, positions = self.patch_manager.get_hyper_block(
+            num_patches_per_group=num_patches_per_group, padding_mode="circular"
+        )
+
+        logging.info("Applying shrinkage...")
+        block = hard_thresholding(block, threshold)
+
+        # manual release of memory
+        memory_cleanup()
+
+        # Aggreation
+        # NOTE: this part needs optimization (numba or parallel or both)
+        logging.info("Aggregating...")
+        aggregate_patches(
+            estimate_denoised_image=self.estimate_denoised_image,
+            weights=weights,
+            hyper_block=block,
+            hyper_block_index=positions,
+        )
+
+        # Normalize by the weights to compute the average
+        self.estimate_denoised_image /= np.maximum(weights, 1)
+
+        # update the patch manager with the new estimate
+        self.patch_manager.background_threshold *= (
+            0.5  # reduce the threshold for background threshold further
+        )
+        self.patch_manager.image = self.estimate_denoised_image
+
+    def re_filtering(
+        self,
+        cut_off_distance: Tuple[int, int],
+        intensity_diff_threshold: float,
+        num_patches_per_group: int,
+    ):
+        """
+        Perform the second step for BM3D, re-filter using estimates as reference noisy free image.
+
+        Parameters
+        ----------
+        cut_off_distance : tuple
+            Maximum spatial distance in terms of row and column indices for patches in the same block.
+        intensity_diff_threshold : float
+            Maximum Euclidean distance in intensity for patches to be considered similar.
+        num_patches_per_group : int
+            The number of patch in each block.
+        """
+        # assume the patch manager has been update to use the estimate_denoised_image
+        # NOTE: this should give us better blocks as we are using a noise reduced image as reference
+        self.group_signal_patches(cut_off_distance, intensity_diff_threshold)
+
+        weights = np.zeros_like(self.image, dtype=np.float64)
+
+        # estimate the noise
+        noise = np.asarray(self.image) - self.estimate_denoised_image
+        sigma_squared = np.mean(noise**2)
+
+        logging.info("Block matching for 2nd pass...")
+        block, positions = self.patch_manager.get_hyper_block(
+            num_patches_per_group=num_patches_per_group,
+            padding_mode="circular",
+            alternative_source=self.image,  # use the original image
+        )
+
+        logging.info("Wiener-Hadamard filtering...")
+        block = wiener_hadamard(block, sigma_squared)
+
+        # manual release of memory
+        memory_cleanup()
+
+        # Aggreation
+        # NOTE: this part needs optimization (numba or parallel or both)
+        logging.info("Aggregating...")
+        aggregate_patches(
+            estimate_denoised_image=self.final_denoised_image,
+            weights=weights,
+            hyper_block=block,
+            hyper_block_index=positions,
+        )
+
+        # Normalize by the weights to compute the average
+        self.final_denoised_image /= np.maximum(weights, 1)
+
+    def denoise(
+        self,
+        cut_off_distance: Tuple[int, int],
+        intensity_diff_threshold: float,
+        num_patches_per_group: int,
+        threshold: float,
+    ):
+        """
+        Perform the BM3D denoising process on the input image.
+
+        Parameters:
+        ----------
+        cut_off_distance : tuple
+            Maximum spatial distance in terms of row and column indices for patches in the same block.
+        intensity_diff_threshold : float
+            Maximum Euclidean distance in intensity for patches to be considered similar.
+        num_patches_per_group : int
+            The number of patch in each block.
+        threshold : float
+            The threshold value for hard thresholding during the first pass.
+        """
+        logging.info("First pass: Hard thresholding")
+        self.thresholding(
+            cut_off_distance, intensity_diff_threshold, num_patches_per_group, threshold
+        )
+
+        logging.info("Second pass: Re-filtering")
+        self.re_filtering(
+            cut_off_distance, intensity_diff_threshold, num_patches_per_group
+        )
+
+
+def bm3d_streak_removal(
+    sinogram: np.ndarray,
+    background_threshold: float = 0.1,
+    patch_size: Tuple[int, int] = (8, 8),
+    stride: int = 3,
+    cut_off_distance: Tuple[int, int] = (64, 64),
+    intensity_diff_threshold: float = 0.1,
+    num_patches_per_group: int = 400,
+    shrinkage_threshold: float = 0.1,
+    k: int = 4,
+) -> np.ndarray:
+    """Multiscale BM3D for streak removal
+
+    Parameters
+    ----------
+    sinogram : np.ndarray
+        The input sinogram to be denoised.
+    background_threshold: float
+        Estimated background intensity threshold, default to 0.1.
+    patch_size : tuple[int, int], optional
+        The size of the patches, by default (8, 8)
+    stride:
+        Steps when generating blocks with sliding window.
+    cut_off_distance : tuple
+        Maximum spatial distance in terms of row and column indices for patches in the same block.
+    intensity_diff_threshold : float, optional
+        The threshold for patch similarity, by default 0.01
+    num_patches_per_group : int
+        The number of patch in each block.
+    shrinkage_threshold : float, optional
+        The threshold for hard thresholding, by default 0.2
+    k : int, optional
+        The number of iterations for horizontal binning, by default 3
+
+    Returns
+    -------
+    np.ndarray
+        The denoised sinogram.
+
+    References
+    ----------
+    [1] ref: `Collaborative Filtering of Correlated Noise <https://doi.org/10.1109/TIP.2020.3014721>`_
+    [2] ref: `Ring artifact reduction via multiscale nonlocal collaborative filtering of spatially correlated noise <https://doi.org/10.1107/S1600577521001910>`_
+    """
+    # step 0: median filter the sinogram
+    sinogram = medfilt2d(sinogram, kernel_size=3)
+    sino_star = sinogram
+
+    # step 1: create a list of binned sinograms
+    binned_sinos = horizontal_binning(sinogram, k=k)
+    # reverse the list
+    binned_sinos = binned_sinos[::-1]
+
+    # step 2: estimate the noise level from the coarsest sinogram, then working back to the original sinogram
+    noise_estimate = None
+    for i in range(len(binned_sinos)):
+        logging.info(f"Processing binned sinogram {i+1} of {len(binned_sinos)}")
+        sino = binned_sinos[i]
+        sino_star = (
+            sino if i == 0 else sino - horizontal_debinning(noise_estimate, sino)
+        )
+
+        if i < len(binned_sinos) - 1:
+            worker = BM3D(
+                image=sino_star,
+                patch_size=patch_size,
+                stride=stride,
+                background_threshold=background_threshold,
+            )
+            worker.denoise(
+                cut_off_distance=cut_off_distance,
+                intensity_diff_threshold=intensity_diff_threshold,
+                num_patches_per_group=num_patches_per_group,
+                threshold=shrinkage_threshold,
+            )
+            noise_estimate = sino - worker.final_denoised_image
+
+    return sino_star
diff --git a/src/bm3dornl/gpu_utils.py b/src/bm3dornl/gpu_utils.py
new file mode 100644
index 0000000..1b301e8
--- /dev/null
+++ b/src/bm3dornl/gpu_utils.py
@@ -0,0 +1,124 @@
+#!/usr/bin/env python3
+"""CuPy utility functions for GPU acceleration."""
+
+import numpy as np
+import cupy as cp
+from cupyx.scipy.linalg import hadamard
+
+
+def hard_thresholding(
+    hyper_block: np.ndarray,
+    threshold: float,
+) -> np.ndarray:
+    """
+    Apply shrinkage operation to a block of image patches on GPU using CuPy.
+
+    This function transforms the block of patches into the frequency domain using FFT,
+    applies hard thresholding to attenuate small coefficients, and then transforms the
+    patches back to the spatial domain to acquire a noise-free estimate.
+
+    Parameters
+    ----------
+    hyper_block : cp.ndarray
+        A 4D CuPy array containing groups of stack of 2D image patches.
+        The shape of `hyper_block` should be (group, n_patches, patch_height, patch_width).
+    threshold : float
+        The threshold value for hard thresholding. Coefficients with absolute values below
+        this threshold will be set to zero.
+
+    Returns
+    -------
+    denoised_block : np.ndarray
+        A 4D CuPy array of the same shape as `hyper_block`, containing the denoised patches.
+
+    Notes
+    -----
+    1. This function uses GPU acceleration to improve the performance of the FFT-based denoising process.
+    2. FFT cache are manually cleared to release memory after each iteration, avoid potential CUDA out of memory error.
+    """
+    # Send data to the GPU
+    hyper_block = cp.asarray(hyper_block)
+
+    # Transform the patch block to the frequency domain using rfft
+    hyper_block = cp.fft.rfft2(hyper_block, axes=(1, 2, 3))
+
+    # Apply hard thresholding
+    hyper_block[cp.abs(hyper_block) < threshold] = 0
+
+    # Transform the block back to the spatial domain using irFFT
+    hyper_block = cp.fft.irfft2(hyper_block, axes=(1, 2, 3))
+
+    # Send data back to the CPU
+    denoised_block = hyper_block.get()
+    del hyper_block
+
+    return denoised_block
+
+
+def wiener_hadamard(hyper_block: np.ndarray, sigma_squared: float):
+    """
+    Wiener filter using the Hadamard transform, implemented with CuPy for GPU acceleration.
+
+    This function handles both 3D and 4D inputs where patches are square and of size 2^n x 2^n.
+
+    Parameters
+    ----------
+    hyper_block : cp.ndarray
+        A 3D or 4D array containing groups of image patches in the **spatial** domain.
+    sigma_squared : float
+        The noise variance.
+
+    Returns
+    -------
+    np.ndarray
+        An array of the same shape as `patch_block`, containing the denoised patches.
+    """
+    # Send data to the GPU
+    hyper_block = cp.asarray(hyper_block)
+
+    # Get the size of the patches
+    n = hyper_block.shape[-1]  # Assuming square patches
+    H = hadamard(n)
+
+    # Flatten 4D to 3D if necessary
+    original_shape = hyper_block.shape
+    if hyper_block.ndim == 4:
+        hyper_block = hyper_block.reshape(-1, n, n)
+
+    # Hadamard transform
+    hyper_block = cp.einsum("ij,kjl->kil", H, hyper_block)
+    hyper_block = cp.einsum("ijk,kl->ijl", hyper_block, H)
+
+    # Calculate mean and variance across the patches dimension
+    local_mean = cp.mean(hyper_block, axis=0, keepdims=True)
+    local_variance = cp.var(hyper_block, axis=0, keepdims=True)
+
+    # Apply Wiener filter
+    hyper_block = (1 - sigma_squared / (local_variance + 1e-8)) * (
+        hyper_block - local_mean
+    ) + local_mean
+    mask = cp.broadcast_to(local_variance < sigma_squared, hyper_block.shape)
+    hyper_block[mask] = 0
+
+    # Inverse Hadamard transform
+    hyper_block = cp.einsum("ij,kjl->kil", H, hyper_block)
+    hyper_block = cp.einsum("ijk,kl->ijl", hyper_block, H) / (n * n)
+
+    # Reshape back if it was 4D
+    if original_shape != hyper_block.shape:
+        hyper_block = hyper_block.reshape(original_shape)
+
+    # Send data back to the CPU
+    denoised_block = hyper_block.get()
+
+    # release memory
+    del hyper_block
+
+    return denoised_block
+
+
+def memory_cleanup():
+    """Clear the memory cache for CuPy and synchronize the default stream."""
+    cp.get_default_memory_pool().free_all_blocks()
+    cp.get_default_pinned_memory_pool().free_all_blocks()
+    cp.cuda.Stream.null.synchronize()
diff --git a/src/bm3dornl/phantom.py b/src/bm3dornl/phantom.py
new file mode 100644
index 0000000..b2522a5
--- /dev/null
+++ b/src/bm3dornl/phantom.py
@@ -0,0 +1,158 @@
+#!/usr/bin/env python3
+"""Sinogram generation for phantom data."""
+
+import numpy as np
+from skimage.transform import radon
+from typing import Tuple
+
+
+def shepp_logan_phantom(size: int = 256, contrast_factor: float = 2.0) -> np.ndarray:
+    """
+    Generate a high-contrast Shepp-Logan phantom with intensity values normalized between 0 and 1.
+
+    Parameters
+    ----------
+    size : int, optional
+        The width and height of the square image, by default 256.
+    contrast_factor : float, optional
+        Factor by which to multiply the intensities to increase contrast, by default 2.0.
+
+    Returns
+    -------
+    np.ndarray
+        A 2D array representing the high-contrast phantom image.
+    """
+    ellipses = [
+        [0.69, 0.92, 0, 0, 0, 2],  # Outer ellipse
+        [0.6624, 0.874, 0, -0.0184, 0, -0.98],
+        [0.21, 0.25, 0.22, 0, -18, -0.2],
+        [0.16, 0.41, -0.22, 0, 18, -0.2],
+        [0.21, 0.25, 0, 0.35, 0, 0.1],
+        [0.046, 0.046, 0, 0.1, 0, 0.2],
+        [0.046, 0.046, 0, -0.1, 0, 0.2],
+        [0.046, 0.023, -0.08, -0.605, 0, 0.2],
+        [0.023, 0.023, 0, -0.606, 0, 0.2],
+        [0.023, 0.046, 0.06, -0.605, 0, 0.2],
+    ]
+
+    phantom = np.zeros((size, size))
+
+    for ellipse in ellipses:
+        a, b, x0, y0, phi, intensity = ellipse
+        intensity *= contrast_factor
+        y, x = np.ogrid[-1 : 1 : size * 1j, -1 : 1 : size * 1j]
+        phi = np.deg2rad(phi)
+        x_rot = x * np.cos(phi) + y * np.sin(phi)
+        y_rot = -x * np.sin(phi) + y * np.cos(phi)
+        mask = ((x_rot - x0) ** 2 / a**2) + ((y_rot - y0) ** 2 / b**2) <= 1
+        phantom += mask * intensity
+
+    min_val = phantom.min()
+    max_val = phantom.max()
+    phantom = (phantom - min_val) / (max_val - min_val)
+
+    return phantom
+
+
+def generate_sinogram(
+    input_img: np.ndarray,
+    scan_step: float,
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Simulate sinogram from input image.
+
+    Parameters
+    ----------
+    input_img : np.ndarray
+        Input image.
+    scan_step : float
+        Scan step in degrees.
+
+    Returns
+    -------
+    sinogram : np.ndarray
+        Generated sinogram.
+    theta : np.ndarray
+        Projection angles in degrees.
+
+    Example
+    -------
+    >>> img = np.random.rand(256, 256)
+    >>> sinogram, thetas_deg = generate_sinogram(img, 1)
+    >>> print(sinogram.shape, thetas_deg.shape)
+    (360, 256) (360,)
+    """
+    # prepare thetas_deg
+    thetas_deg = np.arange(-180, 180, scan_step)
+
+    # prepare sinogram
+    # perform virtual projection via radon transform
+    sinogram = radon(
+        input_img,
+        theta=thetas_deg,
+        circle=True,
+    ).T  # transpose to get the sinogram in the correct orientation for tomopy
+
+    return sinogram, thetas_deg
+
+
+def simulate_detector_gain_error(
+    sinogram: np.ndarray,
+    detector_gain_range: Tuple[float, float],
+    detector_gain_error: float,
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Simulate detector gain error.
+
+    Parameters
+    ----------
+    sinogram : np.ndarray
+        Input sinogram.
+    detector_gain_range : Tuple[float, float]
+        Detector gain range.
+    detector_gain_error : float
+        Detector gain error, along time axis.
+
+    Returns
+    -------
+    sinogram : np.ndarray
+        Sinogram with detector gain error.
+    detector_gain : np.ndarray
+        Detector gain.
+
+    Example
+    -------
+    >>> img = np.random.rand(256, 256)
+    >>> sinogram, thetas_deg = generate_sinogram(img, 1)
+    >>> sinogram, detector_gain = simulate_detector_gain_error(
+    ...     sinogram,
+    ...     (0.9, 1.1),
+    ...     0.1,
+    ... )
+    >>> print(sinogram.shape, detector_gain.shape)
+    (360, 256) (360, 256)
+    """
+    # prepare detector_gain
+    detector_gain = np.random.uniform(
+        detector_gain_range[0],
+        detector_gain_range[1],
+        sinogram.shape[1],
+    )
+    detector_gain = np.ones(sinogram.shape) * detector_gain
+
+    # simulate detector gain vary slightly along time axis
+    if detector_gain_error != 0.0:
+        detector_gain = np.random.normal(
+            detector_gain,
+            detector_gain * detector_gain_error,
+        )
+
+    # apply detector_gain
+    sinogram = sinogram * detector_gain
+
+    # rescale sinogram to [0, 1]
+    sinogram = (sinogram - sinogram.min()) / (sinogram.max() - sinogram.min()) + 1e-8
+
+    # convert to float32
+    sinogram = sinogram.astype(np.float32)
+    detector_gain = detector_gain.astype(np.float32)
+
+    return sinogram, detector_gain
diff --git a/src/bm3dornl/utils.py b/src/bm3dornl/utils.py
new file mode 100644
index 0000000..383dcca
--- /dev/null
+++ b/src/bm3dornl/utils.py
@@ -0,0 +1,269 @@
+#!/usr/bin/env python3
+"""Utility functions for BM3DORNL."""
+
+import numpy as np
+from scipy.interpolate import RectBivariateSpline
+from numba import jit
+from typing import Tuple, List
+
+
+@jit(nopython=True)
+def find_candidate_patch_ids(
+    signal_patches: np.ndarray, ref_index: int, cut_off_distance: Tuple
+) -> List:
+    """
+    Identify candidate patch indices that are within the specified Manhattan distance from a reference patch.
+
+    This function computes a list of indices for patches that are within a given row and column distance from
+    the reference patch specified by `ref_index`. It only considers patches that have not been compared previously
+    (i.e., patches that are ahead of the reference patch in the list, ensuring the upper triangle of the comparison matrix).
+
+    Parameters
+    ----------
+    signal_patches : np.ndarray
+        Array containing the positions of all signal patches. Each position is represented as (row_index, column_index).
+    ref_index : int
+        Index of the reference patch in `signal_patches` for which candidates are being sought.
+    cut_off_distance : tuple
+        A tuple (row_dist, col_dist) specifying the maximum allowed distances in the row and column directions.
+
+    Returns
+    -------
+    list
+        A list of integers representing the indices of the candidate patches in `signal_patches` that are within
+        the `cut_off_distance` from the reference patch and are not previously compared (ensuring upper triangle).
+    """
+    num_patches = signal_patches.shape[0]
+    ref_pos = signal_patches[ref_index]
+    candidate_patch_ids = []
+
+    for i in range(ref_index + 1, num_patches):  # Ensure only checking upper triangle
+        if (
+            np.abs(signal_patches[i, 0] - ref_pos[0]) <= cut_off_distance[0]
+            and np.abs(signal_patches[i, 1] - ref_pos[1]) <= cut_off_distance[1]
+        ):
+            candidate_patch_ids.append(i)
+
+    return candidate_patch_ids
+
+
+@jit(nopython=True)
+def is_within_threshold(
+    ref_patch: np.ndarray, cmp_patch: np.ndarray, intensity_diff_threshold: float
+) -> bool:
+    """
+    Determine if the Euclidean distance between two patches is less than a specified threshold.
+
+    This function computes the Euclidean distance between two patches and checks if it is less than the provided
+    intensity difference threshold. It is optimized with Numba's JIT in nopython mode to ensure high performance.
+
+    Parameters
+    ----------
+    ref_patch : np.ndarray
+        The reference patch as a flattened array of intensities.
+    cmp_patch : np.ndarray
+        The comparison patch as a flattened array of intensities.
+    intensity_diff_threshold : float
+        The threshold below which the Euclidean distance between the patches is considered sufficiently small
+        for the patches to be deemed similar.
+
+    Returns
+    -------
+    bool
+        True if the Euclidean distance between `ref_patch` and `cmp_patch` is less than `intensity_diff_threshold`;
+        otherwise, False.
+
+    Example:
+    --------
+    >>> ref_patch = np.array([1, 2, 3])
+    >>> cmp_patch = np.array([1, 2, 5])
+    >>> threshold = 2.5
+    >>> is_within_threshold(ref_patch, cmp_patch, threshold)
+    False
+    """
+    return np.linalg.norm(ref_patch - cmp_patch) <= intensity_diff_threshold
+
+
+@jit(nopython=True)
+def get_signal_patch_positions(
+    image: np.ndarray,
+    patch_size: Tuple[int, int] = (8, 8),
+    stride: int = 3,
+    background_threshold: float = 0.1,
+) -> np.ndarray:
+    """Segment an image into signal patches.
+
+    Parameters
+    ----------
+    image : np.ndarray
+        The input image to be segmented into patches.
+    patch_size : Tuple[int, int]
+        The size of the patches to be extracted.
+    stride : int
+        The stride for patch extraction.
+    background_threshold : float
+        The threshold for determining background patches.
+
+    Returns
+    -------
+    signal_patches : np.ndarray
+        An array of positions of signal patches.
+
+    NOTE
+    ----
+    Numba has issues with return a Tuple of np.ndarray, and since we only operates on signal patches,
+    we will ignore the background patches for now.
+    """
+    i_height, i_width = image.shape
+    p_height, p_width = patch_size
+
+    signal_patches = []
+
+    for r in range(0, i_height - p_height + 1, stride):
+        for c in range(0, i_width - p_width + 1, stride):
+            patch = image[r : r + p_height, c : c + p_width]
+            patch_max = np.max(patch)
+            if patch_max >= background_threshold:
+                signal_patches.append((r, c))
+
+    # deal with empty list
+    # Note: raise error when couldn't find a single signal patch from the entire
+    #       sinogram, which usually indicating a bad background estimation.
+    if len(signal_patches) == 0:
+        raise ValueError(
+            "Couldn't find any signal patches in the image! Please check the background threshold."
+        )
+
+    return np.array(signal_patches)
+
+
+def pad_patch_ids(
+    candidate_patch_ids: np.ndarray,
+    num_patches: int,
+    mode: str = "circular",
+) -> np.ndarray:
+    """
+    Pad the array of patch IDs to reach a specified length using different strategies.
+
+    Parameters
+    ----------
+    candidate_patch_ids : np.ndarray
+        Array of patch indices identified as candidates.
+    num_patches : int
+        Desired number of patches in the padded list.
+    mode : str
+        Padding mode, options are 'first', 'repeat_sequence', 'circular', 'mirror', 'random'.
+
+    Returns
+    -------
+    np.ndarray
+        Padded array of patch indices.
+    """
+    current_length = len(candidate_patch_ids)
+    if current_length >= num_patches:
+        return candidate_patch_ids[:num_patches]
+
+    if mode == "first":
+        padding = np.full((num_patches - current_length,), candidate_patch_ids[0])
+    elif mode == "repeat_sequence":
+        repeats = (num_patches // current_length) + 1
+        padded = np.tile(candidate_patch_ids, repeats)[:num_patches]
+        return padded
+    elif mode == "circular":
+        extended = np.tile(candidate_patch_ids, ((num_patches // current_length) + 1))[
+            :num_patches
+        ]
+        return extended
+    elif mode == "mirror":
+        mirror_length = min(current_length, num_patches - current_length)
+        mirrored_part = candidate_patch_ids[:mirror_length][::-1]
+        return np.concatenate([candidate_patch_ids, mirrored_part])
+    elif mode == "random":
+        random_padded = np.random.choice(candidate_patch_ids, num_patches, replace=True)
+        return random_padded
+    else:
+        raise ValueError("Unknown padding mode specified.")
+
+    return np.concatenate([candidate_patch_ids, padding])
+
+
+def horizontal_binning(Z: np.ndarray, k: int = 0) -> list[np.ndarray]:
+    """
+    Horizontal binning of the image Z into a list of k images.
+
+    Parameters
+    ----------
+    Z : np.ndarray
+        The image to be binned.
+    k : int
+        Number of iterations to bin the image by half.
+
+    Returns
+    -------
+    list[np.ndarray]
+        List of k images.
+
+    Example
+    -------
+    >>> Z = np.random.rand(64, 64)
+    >>> binned_zs = horizontal_binning(Z, 3)
+    >>> len(binned_zs)
+    4
+    """
+    binned_zs = [Z]
+    for _ in range(k):
+        sub_z0 = Z[:, ::2]
+        sub_z1 = Z[:, 1::2]
+        # make sure z0 and z1 have the same shape
+        if sub_z0.shape[1] > sub_z1.shape[1]:
+            sub_z0 = sub_z0[:, :-1]
+        elif sub_z0.shape[1] < sub_z1.shape[1]:
+            sub_z1 = sub_z1[:, :-1]
+        # average z0 and z1
+        Z = (sub_z0 + sub_z1) * 0.5
+        binned_zs.append(Z)
+    return binned_zs
+
+
+def horizontal_debinning(original_image: np.ndarray, target: np.ndarray) -> np.ndarray:
+    """
+    Horizontal debinning of the image Z into the same shape as Z_target.
+
+    Parameters
+    ----------
+    original_image : np.ndarray
+        The image to be debinned.
+    target : np.ndarray
+        The target image to match the shape.
+
+    Returns
+    -------
+    np.ndarray
+        The debinned image.
+
+    Example
+    -------
+    >>> Z = np.random.rand(64, 64)
+    >>> target = np.random.rand(64, 128)
+    >>> debinned_z = horizontal_debinning(Z, target)
+    >>> debinned_z.shape
+    (64, 128)
+    """
+    # Original dimensions
+    original_height, original_width = original_image.shape
+    # Target dimensions
+    new_height, new_width = target.shape
+
+    # Original grid
+    original_x = np.arange(original_width)
+    original_y = np.arange(original_height)
+
+    # Target grid
+    new_x = np.linspace(0, original_width - 1, new_width)
+    new_y = np.linspace(0, original_height - 1, new_height)
+
+    # Spline interpolation
+    spline = RectBivariateSpline(original_y, original_x, original_image)
+    interpolated_image = spline(new_y, new_x)
+
+    return interpolated_image
diff --git a/src/bm3dornl/version.py b/src/bm3dornl/version.py
deleted file mode 100644
index 6f674ca..0000000
--- a/src/bm3dornl/version.py
+++ /dev/null
@@ -1,8 +0,0 @@
-"""Module to load the version created by versioningit
-
-Will fall back to a default packagename  is not installed"""
-
-try:
-    from ._version import __version__
-except ModuleNotFoundError:
-    __version__ = "0.0.1"
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..fcc77cf
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,16 @@
+# standard imports
+from pathlib import Path
+import pytest
+from shutil import rmtree
+from tempfile import mkdtemp
+
+
+# NOTE: pytest fixtures tmp_path and tmp_path_factory are NOT deleting the temporary directory, hence this fixture
+@pytest.fixture(scope="function")
+def tmpdir():
+    r"""Create directory, then delete the directory and its contents upon test exit"""
+    try:
+        temporary_dir = Path(mkdtemp())
+        yield temporary_dir
+    finally:
+        rmtree(temporary_dir)
diff --git a/tests/data/readme.md b/tests/data/readme.md
new file mode 100644
index 0000000..5decf5f
--- /dev/null
+++ b/tests/data/readme.md
@@ -0,0 +1,3 @@
+# Readme
+
+This folder contains testing data for unit, system and integration tests.
diff --git a/tests/integration/__init__.py b/tests/integration/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/integration/readme.md b/tests/integration/readme.md
new file mode 100644
index 0000000..047a100
--- /dev/null
+++ b/tests/integration/readme.md
@@ -0,0 +1,3 @@
+# Readme
+
+This folder is for integration test.
diff --git a/tests/test_version.py b/tests/test_version.py
deleted file mode 100644
index aa6aec4..0000000
--- a/tests/test_version.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from bm3dornl import __version__
-
-
-def test_version():
-    assert __version__ == "0.0.1"
diff --git a/tests/unit/__init__.py b/tests/unit/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/unit/bm3dornl/test_aggregation.py b/tests/unit/bm3dornl/test_aggregation.py
new file mode 100644
index 0000000..658764b
--- /dev/null
+++ b/tests/unit/bm3dornl/test_aggregation.py
@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+
+"""Unit test for patch aggregation functions."""
+
+import pytest
+import numpy as np
+from bm3dornl.aggregation import aggregate_patches
+
+
+def test_aggregate_patches():
+    # Setup
+    # ph, pw = 2, 2  # patch height and width
+    # num_blocks = 1
+    # num_patches_per_block = 2
+
+    # Create a simple hyper block with known values
+    hyper_block = np.array([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]])
+
+    # Index positions where patches will be placed
+    hyper_block_index = np.array(
+        [
+            [
+                [0, 0],  # First patch at top-left corner
+                [0, 0],  # Second patch also starts at top-left for overlap
+            ]
+        ]
+    )
+
+    # Initial image and weights matrices sized 2x2
+    estimate_denoised_image = np.zeros((2, 2), dtype=float)
+    weights = np.zeros((2, 2), dtype=float)
+
+    # Expected outputs
+    expected_image = np.array(
+        [
+            [6, 8],  # Both patches contribute to the first row
+            [10, 12],  # Both patches contribute to the second row
+        ]
+    )
+    expected_weights = np.array(
+        [
+            [2, 2],  # Both patches contribute to each position
+            [2, 2],
+        ]
+    )
+
+    # Invoke the function under test
+    aggregate_patches(estimate_denoised_image, weights, hyper_block, hyper_block_index)
+
+    # Assertions
+    np.testing.assert_array_almost_equal(
+        estimate_denoised_image,
+        expected_image,
+        err_msg="Image aggregation did not match expected",
+    )
+    np.testing.assert_array_equal(
+        weights, expected_weights, err_msg="Weights update did not match expected"
+    )
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])
diff --git a/tests/unit/bm3dornl/test_block_matching.py b/tests/unit/bm3dornl/test_block_matching.py
new file mode 100644
index 0000000..56ea0f3
--- /dev/null
+++ b/tests/unit/bm3dornl/test_block_matching.py
@@ -0,0 +1,76 @@
+#!/usr/bin/env python3
+"""Unit test for block matching functions."""
+
+import pytest
+import numpy as np
+from bm3dornl.block_matching import PatchManager
+
+
+@pytest.fixture
+def patch_manager():
+    image = np.ones(400).reshape(20, 20).astype(float)  # Simple uniform image
+    patch_size = (5, 5)
+    stride = 5
+    background_threshold = (
+        0.1  # All patches are signal since threshold is lower than image values
+    )
+    manager = PatchManager(image, patch_size, stride, background_threshold)
+    return manager
+
+
+def test_generate_patch_positions(patch_manager):
+    expected_number_of_patches = (20 // 5) * (
+        20 // 5
+    )  # As stride equals the patch size
+    assert (
+        len(patch_manager.signal_patches_pos) == expected_number_of_patches
+    ), "Incorrect number of signal patches generated."
+
+
+def test_get_patch(patch_manager):
+    expected_patch = patch_manager.image[0:5, 0:5]
+    retrieved_patch = patch_manager.get_patch((0, 0))
+    np.testing.assert_array_equal(
+        retrieved_patch, expected_patch, "Patch retrieved incorrectly."
+    )
+
+
+def test_group_signal_patches_geometric(patch_manager):
+    cut_off_distance = (100, 100)  # Larger than image dimensions
+    intensity_diff_threshold = 0.5  # Irrelevant due to uniform image
+    patch_manager.group_signal_patches(cut_off_distance, intensity_diff_threshold)
+    expected_blocks = np.ones(
+        (len(patch_manager.signal_patches_pos), len(patch_manager.signal_patches_pos)),
+        dtype=bool,
+    )
+    np.testing.assert_array_equal(
+        patch_manager.signal_blocks_matrix,
+        expected_blocks,
+        "Signal blocks grouped incorrectly.",
+    )
+
+
+def test_get_hyper_block(patch_manager):
+    cut_off_distance = (100, 100)  # Larger than image dimensions
+    intensity_diff_threshold = 0.5  # Irrelevant due to uniform image
+    patch_manager.group_signal_patches(cut_off_distance, intensity_diff_threshold)
+    num_patches_per_group = 4
+    padding_mode = "circular"
+    patch_groups, positions = patch_manager.get_hyper_block(
+        num_patches_per_group, padding_mode
+    )
+    assert patch_groups.shape == (
+        len(patch_manager.signal_patches_pos),
+        num_patches_per_group,
+        5,
+        5,
+    ), "Incorrect shape of patch groups."
+    assert positions.shape == (
+        len(patch_manager.signal_patches_pos),
+        num_patches_per_group,
+        2,
+    ), "Incorrect shape of positions."
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])
diff --git a/tests/unit/bm3dornl/test_denoiser.py b/tests/unit/bm3dornl/test_denoiser.py
new file mode 100644
index 0000000..876508b
--- /dev/null
+++ b/tests/unit/bm3dornl/test_denoiser.py
@@ -0,0 +1,122 @@
+#!/usr/env/bin python3
+
+"""Unit test for denoiser module."""
+
+import pytest
+import numpy as np
+from unittest.mock import patch
+from bm3dornl.denoiser import (
+    BM3D,
+    bm3d_streak_removal,
+)
+
+
+def test_bm3d_initialization():
+    image = np.random.rand(64, 64)
+    bm3d_instance = BM3D(image)
+    assert np.array_equal(
+        bm3d_instance.image, image
+    ), "The images should be identical after initialization."
+    assert (
+        bm3d_instance.patch_manager is not None
+    ), "PatchManager should be initialized."
+
+
+def test_group_signal_patches():
+    image = np.random.rand(64, 64)
+
+    bm3d_instance = BM3D(image)
+    with patch.object(
+        bm3d_instance.patch_manager, "group_signal_patches"
+    ) as mock_method:
+        bm3d_instance.group_signal_patches((5, 5), 0.1)
+        mock_method.assert_called_once_with((5, 5), 0.1)
+
+
+def test_thresholding():
+    image = np.random.rand(64, 64)
+    bm3d_instance = BM3D(image)
+
+    # Patching the PatchManager instance method get_hyper_block
+    with patch.object(
+        bm3d_instance.patch_manager, "get_hyper_block", autospec=True
+    ) as mock_get_hyper_block, patch.object(
+        bm3d_instance.patch_manager, "_generate_patch_positions", autospec=True
+    ) as mock_generate_patch_positions, patch(
+        "bm3dornl.denoiser.hard_thresholding"
+    ) as mock_hard_thresholding, patch(
+        "bm3dornl.denoiser.aggregate_patches"
+    ) as mock_aggregate_patches, patch(
+        "bm3dornl.denoiser.memory_cleanup"
+    ) as mock_memory_cleanup:
+        # Configure the mock to return specific values
+        mock_get_hyper_block.return_value = (
+            np.random.rand(10, 8, 8),
+            np.random.randint(0, 64, (10, 2)),
+        )
+        mock_hard_thresholding.return_value = np.random.rand(10, 8, 8)
+
+        # Call the method to be tested
+        bm3d_instance.thresholding((5, 5), 0.1, 10, 0.1)
+
+        # Assertions to check if each function was called correctly
+        mock_generate_patch_positions.assert_called_once_with()
+        mock_get_hyper_block.assert_called_once_with(10, padding_mode="circular")
+        mock_hard_thresholding.assert_called_once()
+        mock_aggregate_patches.assert_called_once()
+        mock_memory_cleanup.assert_called_once()
+
+
+@patch("bm3dornl.denoiser.wiener_hadamard")
+@patch("bm3dornl.denoiser.aggregate_patches")
+@patch("bm3dornl.denoiser.memory_cleanup")
+@patch("bm3dornl.denoiser.PatchManager", autospec=True)
+def test_re_filtering(
+    mock_patch_manager,
+    mock_memory_cleanup,
+    mock_aggregate_patches,
+    mock_wiener_hadamard,
+):
+    image = np.random.rand(64, 64)
+    bm3d_instance = BM3D(image)
+    mock_patch_manager.return_value.get_hyper_block.return_value = (
+        np.random.rand(10, 8, 8),
+        np.random.randint(0, 64, (10, 2)),
+    )
+    mock_wiener_hadamard.return_value = np.random.rand(10, 8, 8)
+
+    with patch.object(
+        bm3d_instance, "group_signal_patches"
+    ) as mock_group_signal_patches:
+        bm3d_instance.re_filtering((5, 5), 0.1, 10)
+        mock_group_signal_patches.assert_called_once_with((5, 5), 0.1)
+
+    mock_wiener_hadamard.assert_called_once()
+    mock_aggregate_patches.assert_called_once()
+    mock_memory_cleanup.assert_called_once()
+
+
+@patch("bm3dornl.denoiser.BM3D", autospec=True)
+@patch("bm3dornl.denoiser.horizontal_binning", return_value=np.random.rand(64, 64))
+@patch("bm3dornl.denoiser.horizontal_debinning", return_value=np.random.rand(64, 64))
+@patch("bm3dornl.denoiser.medfilt2d", return_value=np.random.rand(64, 64))
+def test_bm3d_streak_removal(
+    mock_medfilt2d, mock_horizontal_debinning, mock_horizontal_binning, mock_bm3d
+):
+    sinogram = np.random.rand(64, 64)
+    mock_bm3d_instance = mock_bm3d.return_value
+    mock_bm3d_instance.final_denoised_image = np.random.rand(
+        64, 64
+    )  # Set the final_denoised_image attribute
+
+    result = bm3d_streak_removal(sinogram, k=1)
+
+    assert result.shape == (64, 64), "The output should maintain the input dimensions."
+    mock_medfilt2d.assert_called_once_with(sinogram, kernel_size=3)
+    mock_horizontal_binning.assert_called()
+    mock_bm3d.assert_called()
+    mock_horizontal_debinning.assert_called()
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])
diff --git a/tests/unit/bm3dornl/test_gpu_utils.py b/tests/unit/bm3dornl/test_gpu_utils.py
new file mode 100644
index 0000000..dd09ae3
--- /dev/null
+++ b/tests/unit/bm3dornl/test_gpu_utils.py
@@ -0,0 +1,131 @@
+#!/usr/env/bin python3
+
+"""Unit test for cupy utility module."""
+
+import pytest
+import numpy as np
+import cupy as cp
+from bm3dornl.gpu_utils import (
+    hard_thresholding,
+    wiener_hadamard,
+    memory_cleanup,
+)
+
+
+@pytest.mark.cuda_required
+def test_hard_thresholding():
+    # Setup the patch block
+    patch_block = np.random.rand(2, 5, 8, 8)  # Random block of patches on GPU
+    threshold = 0.5  # Threshold for hard thresholding
+
+    # Apply shrinkage
+    denoised_block = hard_thresholding(patch_block, threshold)
+
+    # Convert back to frequency domain to check thresholding
+    dct_block_check = cp.fft.rfft2(cp.asarray(denoised_block), axes=(1, 2, 3)).get()
+
+    # Test if all values in the DCT domain are either zero or above the threshold
+    # Allow a small tolerance for floating point arithmetic issues
+    tolerance = 1e-5
+    assert np.all(
+        (np.abs(dct_block_check) >= threshold - tolerance)
+        | (np.abs(dct_block_check) < tolerance)
+    ), "DCT coefficients are not correctly thresholded"
+
+    # Check the shape is maintained
+    assert (
+        patch_block.shape == denoised_block.shape
+    ), "Output shape does not match input shape"
+
+    # Check for any values that should not have been zeroed out
+    original_dct_block = cp.fft.rfft2(cp.asarray(patch_block), axes=(1, 2, 3)).get()
+    should_not_change = np.abs(original_dct_block) >= threshold
+    assert np.allclose(
+        dct_block_check[should_not_change],
+        original_dct_block[should_not_change],
+        atol=tolerance,
+    ), "Values that should not have been zeroed out have changed"
+
+    # Cleanup GPU memory
+    memory_cleanup()
+
+
+@pytest.mark.cuda_required
+def test_wiener_hadamard_3d_input():
+    # Prepare a 3D patch block
+    patch_block = np.random.rand(1000, 8, 8)  # 1000 patches of 8x8 pixels
+    sigma_squared = 0.1
+
+    # Apply the Wiener-Hadamard filter
+    denoised_block = wiener_hadamard(patch_block, sigma_squared)
+
+    # Check if the output dimensions match the input
+    assert (
+        patch_block.shape == denoised_block.shape
+    ), "Output dimensions should match input dimensions"
+
+    # Ensure changes were made to the patch block
+    assert not cp.allclose(
+        patch_block, denoised_block, atol=1e-3
+    ), "No changes detected in the patch block after filtering"
+
+    # Cleanup GPU memory
+    memory_cleanup()
+
+
+@pytest.mark.cuda_required
+def test_wiener_hadamard_4d_input():
+    # Prepare a 4D patch block
+    patch_block = np.random.rand(
+        4, 1000, 8, 8
+    )  # 4 batches, 1000 patches each, of 8x8 pixels
+    sigma_squared = 0.1
+
+    # Apply the Wiener-Hadamard filter
+    denoised_block = wiener_hadamard(patch_block, sigma_squared)
+
+    # Check if the output dimensions match the input
+    assert (
+        patch_block.shape == denoised_block.shape
+    ), "Output dimensions should match input dimensions"
+
+    # Ensure changes were made to the patch block
+    assert not np.allclose(
+        patch_block, denoised_block, atol=1e-3
+    ), "No changes detected in the patch block after filtering"
+
+    # Cleanup GPU memory
+    memory_cleanup()
+
+
+@pytest.mark.cuda_required
+def test_memory_cleanup(mocker):
+    # Create mock objects for the method chains
+    mock_free_all_blocks = mocker.Mock()
+    mock_free_all_blocks_pinned = mocker.Mock()
+    mock_synchronize = mocker.Mock()
+
+    # Mock the chain calls
+    mock_memory_pool = mocker.patch(
+        "cupy.get_default_memory_pool", return_value=mock_free_all_blocks
+    )
+    mock_memory_pool().free_all_blocks = mock_free_all_blocks
+
+    mock_pinned_memory_pool = mocker.patch(
+        "cupy.get_default_pinned_memory_pool", return_value=mock_free_all_blocks_pinned
+    )
+    mock_pinned_memory_pool().free_all_blocks = mock_free_all_blocks_pinned
+
+    mocker.patch("cupy.cuda.Stream.null.synchronize", mock_synchronize)
+
+    # Call the function
+    memory_cleanup()
+
+    # Check if the functions were called
+    mock_free_all_blocks.assert_called_once()
+    mock_free_all_blocks_pinned.assert_called_once()
+    mock_synchronize.assert_called_once()
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])
diff --git a/tests/unit/bm3dornl/test_phantom.py b/tests/unit/bm3dornl/test_phantom.py
new file mode 100644
index 0000000..800cc07
--- /dev/null
+++ b/tests/unit/bm3dornl/test_phantom.py
@@ -0,0 +1,94 @@
+#!/usr/bin/env python3
+
+"""Unit test for phantom module."""
+
+import pytest
+import numpy as np
+from bm3dornl.phantom import (
+    shepp_logan_phantom,
+    generate_sinogram,
+    simulate_detector_gain_error,
+)
+
+
+def test_shepp_logan_phantom():
+    """Test the shepp_logan_phantom function."""
+    size = 256
+    contrast_factor = 2.0
+    phantom = shepp_logan_phantom(size=size, contrast_factor=contrast_factor)
+
+    # Check the shape
+    assert phantom.shape == (size, size), "Phantom shape mismatch"
+
+    # Check that all values are between 0 and 1
+    assert phantom.min() >= 0, "Phantom values should be >= 0"
+    assert phantom.max() <= 1, "Phantom values should be <= 1"
+
+    # Check that the phantom contains meaningful non-zero values
+    assert np.any(phantom > 0), "Phantom should have non-zero values"
+
+
+def test_generate_sinogram():
+    """Test the generate_sinogram function."""
+    input_size = 256
+    scan_step = 1.0
+    input_img = np.random.rand(input_size, input_size)
+
+    sinogram, thetas_deg = generate_sinogram(input_img, scan_step)
+
+    # Verify the shape of the sinogram
+    expected_num_projections = int(360 / scan_step)
+    assert sinogram.shape == (
+        expected_num_projections,
+        input_size,
+    ), f"Sinogram shape mismatch, expected: {(expected_num_projections, input_size)}"
+
+    # Verify the length of the angles array
+    assert thetas_deg.shape == (
+        expected_num_projections,
+    ), f"Theta shape mismatch, expected: {(expected_num_projections,)}"
+
+    # Ensure that the theta array spans the correct range
+    assert thetas_deg.min() >= -180, "Minimum theta value should be -180 degrees"
+    assert thetas_deg.max() < 180, "Maximum theta value should be less than 180 degrees"
+
+    # Check for non-zero sinogram
+    assert np.any(sinogram > 0), "The sinogram should contain non-zero values"
+
+
+def test_simulate_detector_gain_error():
+    """Test the simulate_detector_gain_error function."""
+    # Define the parameters for the test
+    sinogram_shape = (360, 256)
+    detector_gain_range = (0.9, 1.1)
+    detector_gain_error = 0.1
+
+    # Create a random sinogram for testing
+    sinogram = np.random.rand(*sinogram_shape)
+
+    # Call the function to simulate gain error
+    modified_sinogram, detector_gain = simulate_detector_gain_error(
+        sinogram, detector_gain_range, detector_gain_error
+    )
+
+    # Ensure the output sinogram and detector gain have the same shape as the input
+    assert (
+        modified_sinogram.shape == sinogram_shape
+    ), f"Output sinogram shape mismatch, expected: {sinogram_shape}"
+    assert (
+        detector_gain.shape == sinogram_shape
+    ), f"Detector gain shape mismatch, expected: {sinogram_shape}"
+
+    # Check that the sinogram is normalized to [0, 1]
+    assert modified_sinogram.min() >= 0, "Sinogram values should be >= 0"
+    assert modified_sinogram.max() <= 1, "Sinogram values should be <= 1"
+
+    # Ensure that the output is of type float32
+    assert (
+        modified_sinogram.dtype == np.float32
+    ), "Output sinogram should be of type float32"
+    assert detector_gain.dtype == np.float32, "Detector gain should be of type float32"
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])
diff --git a/tests/unit/bm3dornl/test_utils.py b/tests/unit/bm3dornl/test_utils.py
new file mode 100644
index 0000000..27445f8
--- /dev/null
+++ b/tests/unit/bm3dornl/test_utils.py
@@ -0,0 +1,232 @@
+#!/usr/bin/env python3
+
+"""Unit tests for the utility module."""
+
+import pytest
+import numpy as np
+from bm3dornl.utils import (
+    find_candidate_patch_ids,
+    is_within_threshold,
+    get_signal_patch_positions,
+    pad_patch_ids,
+    horizontal_binning,
+    horizontal_debinning,
+)
+
+
+def test_find_candidate_patch_ids():
+    # Setup the signal patches and test various reference indices and cut-off distances
+    signal_patches = np.array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [2, 2], [3, 3]])
+
+    # Test case 1
+    ref_index = 0
+    cut_off_distance = (1, 1)
+    expected = [
+        1,
+        3,
+        4,
+    ]  # Only patches within 1 unit from (0, 0) in both dimensions and are after index 0
+    result = find_candidate_patch_ids(signal_patches, ref_index, cut_off_distance)
+    assert result == expected, "Test case 1 failed"
+
+    # Test case 2
+    ref_index = 2
+    cut_off_distance = (2, 2)
+    expected = [3, 4, 5]  # Indices that are within 2 units from (0, 2)
+    result = find_candidate_patch_ids(signal_patches, ref_index, cut_off_distance)
+    assert result == expected, "Test case 2 failed"
+
+    # Test case 3
+    ref_index = 4
+    cut_off_distance = (3, 3)
+    expected = [5, 6]  # Indices that are within 3 units from (1, 1)
+    result = find_candidate_patch_ids(signal_patches, ref_index, cut_off_distance)
+    assert result == expected, "Test case 3 failed"
+
+    # Test case 4
+    ref_index = 0
+    cut_off_distance = (0, 0)
+    expected = []  # No patch within 0 distance from (0, 0) except itself
+    result = find_candidate_patch_ids(signal_patches, ref_index, cut_off_distance)
+    assert result == expected, "Test case 4 failed"
+
+
+def test_is_within_threshold():
+    # Setup the patches
+    ref_patch = np.array([1, 2, 3], dtype=float)
+    cmp_patch_same = np.array([1, 2, 3], dtype=float)
+    cmp_patch_different = np.array([4, 5, 6], dtype=float)
+    cmp_patch_close = np.array([1, 2, 4], dtype=float)
+
+    # Test case 1: Same patches, zero distance
+    threshold = 0
+    result = is_within_threshold(ref_patch, cmp_patch_same, threshold)
+    assert result, "Failed: Same patches should be within zero distance"
+
+    # Test case 2: Different patches, threshold less than actual distance
+    threshold = 2
+    result = is_within_threshold(ref_patch, cmp_patch_different, threshold)
+    assert not result, "Failed: Different patches should not be within distance of 2"
+
+    # Test case 3: Different patches, threshold greater than actual distance
+    threshold = 6
+    result = is_within_threshold(ref_patch, cmp_patch_different, threshold)
+    assert result, "Failed: Different patches should be within distance of 6"
+
+    # Test case 4: Slightly different patches, small threshold
+    threshold = 2
+    result = is_within_threshold(ref_patch, cmp_patch_close, threshold)
+    assert result, "Failed: Slightly different patches should be within distance of 2"
+
+    # Test case 5: Slightly different patches, very small threshold
+    threshold = 0.1
+    result = is_within_threshold(ref_patch, cmp_patch_close, threshold)
+    assert not result, "Failed: Slightly different patches should not be within very small distance of 0.1"
+
+
+def test_get_signal_patch_positions():
+    # Create a synthetic image with a signal patch in the center
+    image = np.zeros((10, 10), dtype=float)
+    image[4:6, 4:6] = 1.0  # Making the center bright
+
+    # Define the patch size, stride, and background threshold
+    patch_size = (3, 3)
+    stride = 1
+    background_threshold = 0.5
+
+    # Call the function
+    result = get_signal_patch_positions(
+        image=image,
+        patch_size=patch_size,
+        stride=stride,
+        background_threshold=background_threshold,
+    )
+
+    # Check that the function correctly identified the signal patch
+    assert (
+        [4, 4] in result.tolist()
+    ), "The signal patch at position (4, 4) was not identified correctly"
+
+
+def test_get_signal_patch_positions_no_signal_error():
+    # Create an image with all values below the threshold
+    image = np.zeros((10, 10), dtype=float)
+    patch_size = (3, 3)
+    stride = 1
+    background_threshold = 0.5
+
+    # Check for ValueError when no signal patches are found
+    with pytest.raises(ValueError) as excinfo:
+        get_signal_patch_positions(
+            image=image,
+            patch_size=patch_size,
+            stride=stride,
+            background_threshold=background_threshold,
+        )
+    assert "Couldn't find any signal patches in the image" in str(
+        excinfo.value
+    ), "Expected ValueError for no signal patches was not raised"
+
+
+def test_pad_patch_ids_first():
+    candidate_patch_ids = np.array([1, 2, 3])
+    num_patches = 5
+    padded = pad_patch_ids(candidate_patch_ids, num_patches, mode="first")
+    assert np.array_equal(
+        padded, np.array([1, 2, 3, 1, 1])
+    ), "Padding with the first element failed"
+
+
+def test_pad_patch_ids_repeat_sequence():
+    candidate_patch_ids = np.array([1, 2, 3])
+    num_patches = 7
+    padded = pad_patch_ids(candidate_patch_ids, num_patches, mode="repeat_sequence")
+    assert np.array_equal(
+        padded, np.array([1, 2, 3, 1, 2, 3, 1])
+    ), "Repeating sequence padding failed"
+
+
+def test_pad_patch_ids_circular():
+    candidate_patch_ids = np.array([1, 2, 3])
+    num_patches = 6
+    padded = pad_patch_ids(candidate_patch_ids, num_patches, mode="circular")
+    assert np.array_equal(
+        padded, np.array([1, 2, 3, 1, 2, 3])
+    ), "Circular padding failed"
+
+
+def test_pad_patch_ids_mirror():
+    candidate_patch_ids = np.array([1, 2, 3])
+    num_patches = 6
+    padded = pad_patch_ids(candidate_patch_ids, num_patches, mode="mirror")
+    assert np.array_equal(padded, np.array([1, 2, 3, 3, 2, 1])), "Mirror padding failed"
+
+
+def test_pad_patch_ids_random():
+    candidate_patch_ids = np.array([1, 2, 3])
+    num_patches = 5
+    padded = pad_patch_ids(candidate_patch_ids, num_patches, mode="random")
+    # Check that all elements in padded are from candidate_patch_ids
+    assert all(item in candidate_patch_ids for item in padded), "Random padding failed"
+
+
+def test_pad_patch_ids_unknown_mode():
+    candidate_patch_ids = np.array([1, 2, 3])
+    num_patches = 5
+    with pytest.raises(ValueError) as excinfo:
+        pad_patch_ids(candidate_patch_ids, num_patches, mode="unknown")
+    assert "Unknown padding mode specified" in str(
+        excinfo.value
+    ), "Error not raised for unknown mode"
+
+
+def test_horizontal_binning():
+    # Initial setup: Create a test image
+    Z = np.random.rand(64, 64)
+
+    # Number of binning iterations
+    k = 3
+
+    # Perform the binning
+    binned_images = horizontal_binning(Z, k)
+
+    # Assert we have the correct number of images
+    assert len(binned_images) == k + 1, "Incorrect number of binned images returned"
+
+    # Assert that each image has the correct dimensions
+    expected_width = 64
+    for i, img in enumerate(binned_images):
+        assert img.shape[0] == 64, f"Height of image {i} is incorrect"
+        assert img.shape[1] == expected_width, f"Width of image {i} is incorrect"
+        expected_width = (expected_width + 1) // 2  # Calculate the next expected width
+
+
+def test_horizontal_binning_k_zero():
+    Z = np.random.rand(64, 64)
+    binned_images = horizontal_binning(Z, 0)
+    assert len(binned_images) == 1 and np.array_equal(
+        binned_images[0], Z
+    ), "Binning with k=0 should return only the original image"
+
+
+def test_horizontal_binning_large_k():
+    Z = np.random.rand(64, 64)
+    binned_images = horizontal_binning(Z, 6)
+    assert len(binned_images) == 7, "Incorrect number of images for large k"
+    assert binned_images[-1].shape[1] == 1, "Final image width should be 1 for large k"
+
+
+@pytest.mark.parametrize(
+    "original_width, target_width", [(32, 64), (64, 128), (128, 256)]
+)
+def test_horizontal_debinning_scaling(original_width, target_width):
+    original_image = np.random.rand(64, original_width)
+    target_shape = (64, target_width)
+    debinned_image = horizontal_debinning(original_image, np.empty(target_shape))
+    assert (
+        debinned_image.shape == target_shape
+    ), f"Failed to scale from {original_width} to {target_width}"
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])
diff --git a/tests/unit/readme.md b/tests/unit/readme.md
new file mode 100644
index 0000000..b53e7a2
--- /dev/null
+++ b/tests/unit/readme.md
@@ -0,0 +1,3 @@
+# Readme
+
+This folder is for unit test.