RIDNet: Recursive Information Distillation Network for Color Image Denoising

Shengkai Zhuo, Zhi Jin, Wenbin Zou, Xia Li

ICCVW 2019 pp. 3896-3903

doi:10.1109/ICCVW.2019.00483 /iccvw/2019/zhuo2019iccvw-ridnet/

Abstract

Color image denoising is more challenging in effectiveness when compared with the grayscale one. Most existing methods play a certain role in efficiency or flexibility, but lack robustness to handle various noise levels, especially the severe noise. This keeps them away from being practically applied to color image denoising. To address this issue, we propose a robust CNN based denoiser, namely Recursive Information Distillation Network (RIDNet), to handle the denoising task at high noise levels. The proposed RIDNet simultaneously keeps the efficiency and flexibility by introducing the information distillation module and merging a tunable noise level map as the input, respectively. Experiment results on Additive White Gaussian Noise (AWGN) images demonstrate that our method outperforms most of the state-of-the-art color image denoisers.

ICCVW Semantic Scholar

Cite

Text

Zhuo et al. "RIDNet: Recursive Information Distillation Network for Color Image Denoising." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00483

Markdown

[Zhuo et al. "RIDNet: Recursive Information Distillation Network for Color Image Denoising." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/zhuo2019iccvw-ridnet/) doi:10.1109/ICCVW.2019.00483

BibTeX

@inproceedings{zhuo2019iccvw-ridnet,
  title     = {{RIDNet: Recursive Information Distillation Network for Color Image Denoising}},
  author    = {Zhuo, Shengkai and Jin, Zhi and Zou, Wenbin and Li, Xia},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2019},
  pages     = {3896-3903},
  doi       = {10.1109/ICCVW.2019.00483},
  url       = {https://mlanthology.org/iccvw/2019/zhuo2019iccvw-ridnet/}
}