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An iterative regularization method for total variation-based image restoration

Stanley Osher Martin Burger Donald Goldfarb Jinjun Xu Wotao Yin

Numerical Analysis and Scientific Computing mathscidoc:1912.43768

Multiscale Modeling & Simulation, 4, (2), 460-489, 2005
We introduce a new iterative regularization procedure for inverse problems based on the use of Bregman distances, with particular focus on problems arising in image processing. We are motivated by the problem of restoring noisy and blurry images via variational methods by using total variation regularization. We obtain rigorous convergence results and effective stopping criteria for the general procedure. The numerical results for denoising appear to give significant improvement over standard models, and preliminary results for deblurring/denoising are very encouraging.
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@inproceedings{stanley2005an,
  title={An iterative regularization method for total variation-based image restoration},
  author={Stanley Osher, Martin Burger, Donald Goldfarb, Jinjun Xu, and Wotao Yin},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224205654495685332},
  booktitle={Multiscale Modeling & Simulation},
  volume={4},
  number={2},
  pages={460-489},
  year={2005},
}
Stanley Osher, Martin Burger, Donald Goldfarb, Jinjun Xu, and Wotao Yin. An iterative regularization method for total variation-based image restoration. 2005. Vol. 4. In Multiscale Modeling & Simulation. pp.460-489. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224205654495685332.
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