Artifacts reduction in JPEG-Compressed Images using CNNs.

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).

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.

1. Python 3.6.5
2. TensorFlow 1.1.0.
3. Keras 2.2.2.
4. Matlab.
5. Matconvnet.

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

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, and then put the .mat file in models folder which in test folder.
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.

Qualitative evaluation of reconstruction quality using different networks for JPEG quality quality = 10 and quality =20.