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A fast algorithm for edge-preserving variational multichannel image restoration

Junfeng Yang Wotao Yin Yin Zhang Yilun Wang

Numerical Analysis and Scientific Computing mathscidoc:1912.43773

SIAM Journal on Imaging Sciences, 2, (2), 569-592, 2009.5
Variational models with \ell_1-norm based regularization, in particular total variation (TV) and its variants, have long been known to offer superior image restoration quality, but processing speed remained a bottleneck, preventing their widespread use in the practice of color image processing. In this paper, by extending the grayscale image deblurring algorithm proposed in [Y. Wang, J. Yang, W. Yin, and Y. Zhang, <i>SIAM J. Imaging Sci.</i>, 1 (2008), pp. 248272], we construct a simple and efficient algorithm for multichannel image deblurring and denoising, applicable to both within-channel and cross-channel blurs in the presence of additive Gaussian noise. The algorithm restores an image by minimizing an energy function consisting of an \ell_1-norm fidelity term and a regularization term that can be either TV, weighted TV, or regularization functions based on higher-order derivatives. Specifically, we use a multichannel
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@inproceedings{junfeng2009a,
  title={A fast algorithm for edge-preserving variational multichannel image restoration},
  author={Junfeng Yang, Wotao Yin, Yin Zhang, and Yilun Wang},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224205715457877337},
  booktitle={SIAM Journal on Imaging Sciences},
  volume={2},
  number={2},
  pages={569-592},
  year={2009},
}
Junfeng Yang, Wotao Yin, Yin Zhang, and Yilun Wang. A fast algorithm for edge-preserving variational multichannel image restoration. 2009. Vol. 2. In SIAM Journal on Imaging Sciences. pp.569-592. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224205715457877337.
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