Building Dual-Domain Representations for Compression Artifacts Reduction

@inproceedings{Guo2016BuildingDR,
  title={Building Dual-Domain Representations for Compression Artifacts Reduction},
  author={Jun Guo and Hongyang Chao},
  booktitle={ECCV},
  year={2016}
}
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… CONTINUE READING
BETA

Similar Papers

Topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 32 CITATIONS

One-To-Many Network for Visually Pleasing Compression Artifacts Reduction

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
VIEW 11 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

Multi-frame Quality Enhancement for Compressed Video

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • 2018
VIEW 4 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED