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Zero norm based analysis model for image smoothing and reconstruction

Jiebo Song School of Mathematics, Sun Yat-Sen University, Guangzhou, People’s Republic of China Jia Li School of Mathematics, Sun Yat-Sen University, Guangzhou, People’s Republic of China Zhengan Yao School of Mathematics, Sun Yat-Sen University, Guangzhou, People’s Republic of China Kaisheng Ma Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, People’s Republic of China Chenglong Bao Yau Mathematical Sciences Center, Tsinghua University, Beijing, People’s Republic of China

TBD mathscidoc:2203.43026

Inverse Problems, 36, (11), 2020.10
The sparsity-based approaches have demonstrated promising performance in image processing. In this paper, for better preservation of the salient edge structures of images, we propose an l_0 + l_2-norm based analysis model, which requires solving a challenging non-separable l0-norm related minimiza- tion problem, and we also propose an inexact augmented Lagrangian method with proven convergence to a local minimum. Extensive experiments in image smoothing, including texture removal and context smoothing, show that our method achieves better visual results over various sparsity-based models and the CNN method. Also, experiments on sparse view CT reconstruction further validate the advantage of the proposed method.
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@inproceedings{jiebo2020zero,
  title={Zero norm based analysis model for image smoothing and reconstruction},
  author={Jiebo Song, Jia Li, Zhengan Yao, Kaisheng Ma, and Chenglong Bao},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220316112533422938980},
  booktitle={Inverse Problems},
  volume={36},
  number={11},
  year={2020},
}
Jiebo Song, Jia Li, Zhengan Yao, Kaisheng Ma, and Chenglong Bao. Zero norm based analysis model for image smoothing and reconstruction. 2020. Vol. 36. In Inverse Problems. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220316112533422938980.
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