Building Dual Domain Representations for Compression Artifacts Reduction
We propose a highly accurate approach to remove artifacts of JPEG compressed images. Our approach jointly learns a very deep convolutional network in both DCT and pixel domains. The dual domain representation can make full use of DCT domain prior knowledge of JPEG compression, which is usually lacking in traditional network based approaches. At the same time, it can also benefit from the prowess and the efficiency of the deep feed forward architecture, in comparison to capacity limited sparse coding based approaches. Two simple strategies, i.e. Adam and residual learning, are adopted to train the very deep network and later proved to be a success. Extensive experiments demonstrate the large improvements of our approach over the state of the arts.
ECCV