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Fatma-ALbluwi edited this page Oct 1, 2018 · 6 revisions

The proposed models DACAR Network/ SACAR Network

In this work, we consider both types of CNN architectures to reduce the artifacts (e.g.; blurring, blocking artifacts and ringing artifacts) in JPEG compressed images. We have proposed two CNNs which we refer to as Direct Architecture Compression Artifacts Removal (DA-CAR) network and Skip Architecture Compression Artifacts Removal (SA-CAR).

  • For DA-CAR version, we experiment with 3, 4, and 5 layer architectures (DACAR3, DA-CAR4 and DA-CAR5, respectively).
  • For the skip-based architecture we consider 6 layers (SACAR6), the third layer is concatenating activation (feature maps) between the first layer and the second layer (2+1).

Direct and Skip Architectures.

networks

Network-Keras

These codes are to reduce the different artifacts from JPEG compressed images.

If these codes are helpful for you, please cite this paper: Artifacts reduction in JPEG-Compressed Images using CNNs, F. Albluwi, V. Krylov and R. Dahyot Irish Machine Vision and Image Processing conference (IMVIP 2018 https://www.ulster.ac.uk/conference/imvip-2018), 2018. Published in IMVIP e-book of proceedings with ISBN 978-0-9934207-3-3.

Dependencies

  1. TensorFlow.
  2. Keras.
  3. Matconvnet.

Generating data

  1. reduce the quality of images at different levels (JPEG_Quality = 10 or 20) by using 'Quality' function.
  2. The training set is 400 images from the BSDS500 (The Berkeley Segmentation Dataset).

Training

  1. generate training patches using Matlab: run generate_train.m and generate_test.m which in train folder, and then put this folder in the network folder you want to train (._train as SA-CAR6_train).
  2. use Keras with TensorFlow (tf) as a backend to train any model (DACAR3, DACAR4, DACAR5 or SA-CAR6); Adam is used to optimizing the network for fast convergence: run DACAR_train.py or SACAR_train.py to produce DACAR model / or SACAR model.
  3. convert Keras model to .Mat for testing using Matconvnet: run load_save.py first, then run save_model.m to produce Matconvnet model.
  4. run DACAR_SACAR_test.m in “test” folder to test the model; Live1 (which contains 29 images) and BSD100 (which contains 100 images) are used as testing data.

Note

This code is based on Keras-2.

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