JPEG Artifacts Reduction via Deep Convolutional Sparse Coding

Xueyang Fu; Zheng-Jun Zha; Feng Wu; Xinghao Ding; John Paisley

ICCV 2019poster139 citations

JPEG Artifacts Reduction via Deep Convolutional Sparse Coding

Xueyang Fu, Zheng-Jun Zha, Feng Wu, Xinghao Ding, John Paisley

Abstract

To effectively reduce JPEG compression artifacts, we propose a deep convolutional sparse coding (DCSC) network architecture. We design our DCSC in the framework of classic learned iterative shrinkage-threshold algorithm. To focus on recognizing and separating artifacts only, we sparsely code the feature maps instead of the raw image. The final de-blocked image is directly reconstructed from the coded features. We use dilated convolution to extract multi-scale image features, which allows our single model to simultaneously handle multiple JPEG compression levels. Since our method integrates model-based convolutional sparse coding with a learning-based deep neural network, the entire network structure is compact and more explainable. The resulting lightweight model generates comparable or better de-blocking results when compared with state-of-the-art methods.

BibTeX

@inproceedings{iccv2019_jpegartifactsred,
  title = {JPEG Artifacts Reduction via Deep Convolutional Sparse Coding},
  author = {Xueyang Fu and Zheng-Jun Zha and Feng Wu and Xinghao Ding and John Paisley},
  booktitle = {ICCV 2019},
  year = {2019}
}