JPEG Artifacts Reduction via Deep Convolutional Sparse Coding

Xueyang Fu, Zheng-Jun Zha, Feng Wu, Xinghao Ding, John Paisley; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 2501-2510

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.

Related Material


[pdf]
[bibtex]
@InProceedings{Fu_2019_ICCV,
author = {Fu, Xueyang and Zha, Zheng-Jun and Wu, Feng and Ding, Xinghao and Paisley, John},
title = {JPEG Artifacts Reduction via Deep Convolutional Sparse Coding},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}