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An image restoration model combining mixed L1/L2 fidelity terms

Tongtong Jia North China Electric Power University Yuying Shi North China Electric Power University Yonggui Zhu Communication University of China Lei Wang North China Electric Power University

Numerical Analysis and Scientific Computing mathscidoc:1702.25074

Journal of Visual Communication and Image Representation, 38, (7), 461-473, 2016.7
Image restoration is a common problem in visual process. In this paper, a modified minimization model is presented, which combines the L1 and L2 fidelity terms with a combined quadratic L2 and TV regularizer just as the regularizer of Cai et al. (2013). The combined regularizer has the priorities of preserving desirable edges and ensuring several kinds of noises can be removed clearly. Split-Bregman algorithm is effi- ciently employed to solve this model and convergence analysis is also discussed. Moreover, we extend the proposed model and algorithm for image restoration involving blurry images and color images. Experimental results show that our proposed model and algorithm have good performance both in visual and ISNR values for different kinds of blurs and noises including mixed noise.
Image restoration; L1 and L2 fidelity terms; TV; Split-Bregman; Mixed noise
[ Download ] [ 2017-02-10 02:17:31 uploaded by ygzhu ] [ 819 downloads ] [ 0 comments ] 
@inproceedings{tongtong2016an,
  title={An image restoration model combining mixed L1/L2 fidelity terms},
  author={Tongtong Jia, Yuying Shi, Yonggui Zhu, and Lei Wang},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170210021731264985417},
  booktitle={Journal of Visual Communication and Image Representation},
  volume={38},
  number={7},
  pages={461-473},
  year={2016},
}
Tongtong Jia, Yuying Shi, Yonggui Zhu, and Lei Wang. An image restoration model combining mixed L1/L2 fidelity terms. 2016. Vol. 38. In Journal of Visual Communication and Image Representation. pp.461-473. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170210021731264985417.
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