tutorial31_image_sharpening_using_unsharp_mask.py

#Video Playlist: https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG

"""
Unsharp mask enhances edges by subtracting an unsharp (smoothed) version of the image from the original.
Effectively making the filter a high pass filter. 

enhanced image = original + amount * (original - blurred)

Amount of sharpening can be controlled via scaling factor, a multiplication factor
for the sharpened signal. 

skimage uses Gaussian smoothing for image blurring therefore the radius parameter 
in the unsharp masking filter refers to the sigma parameter of the gaussian filter.
"""

#This code shows that unsharp is nothing but original + amount *(original-blurred)
from skimage import io, img_as_float
from skimage.filters import unsharp_mask
from skimage.filters import gaussian

img = img_as_float(io.imread("images/sandstone_blur_2sigma.tif", as_gray=True))

gaussian_img = gaussian(img, sigma=2, mode='constant', cval=0.0)

img2 = (img - gaussian_img)*2.

img3 = img + img2

from matplotlib import pyplot as plt
plt.imshow(img3, cmap="gray")


"""
from skimage import io
from skimage.filters import unsharp_mask

img = io.imread("images/sandstone_blur_2sigma.tif")

#Radius defines the degree of blurring
#Amount defines the multiplication factor for original - blurred image
unsharped_img = unsharp_mask(img, radius=3, amount=2)


import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12, 12))
ax1 = fig.add_subplot(1,2,1)
ax1.imshow(img, cmap='gray')
ax1.title.set_text('Input Image')
ax2 = fig.add_subplot(1,2,2)
ax2.imshow(unsharped_img, cmap='gray')
ax2.title.set_text('Unsharped Image')

plt.show()
"""