Iterative Nonlocal Total Variation Regularization Method for Image Restoration

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Abstract

In this paper, a Bregman iteration based total variation image restoration algorithm is proposed. Based on the Bregman iteration, the algorithm splits the original total variation problem into sub-problems that are easy to solve. Moreover, non-local regularization is introduced into the proposed algorithm, and a method to choose the non-local filter parameter locally and adaptively is proposed. Experiment results show that the proposed algorithms outperform some other regularization methods. © 2013 Xu et al.

Figures

  • Figure 2. Restoration results on a 256|256 Cameraman image degraded by a 7|7 Gaussian kernel with sigma~3 and a gaussian noise with s~2. A. Original Image B. Degraded Image C. Operator Splitting TV D. ForWard E. FTVd F. FAST-TV G. NLTV+BOS H. Algorithm 1 I. Algorithm 2. doi:10.1371/journal.pone.0065865.g002
  • Figure 3. Restoration results on a 256|256 Cameraman image degraded by a 9|9 average kernel and a gaussian noise with s~2. A. Original Image B. Degraded Image C. Operator Splitting TV D. ForWard E. FTVd F. FAST-TV G. NLTV+BOS H. Algorithm 1 I. Algorithm 2. doi:10.1371/journal.pone.0065865.g003
  • Table 1. PSNR and SSIM results of the methods on five different images with a 7|7 Gaussian kernel with sigma~3 and gaussian noise s~2.

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CITATION STYLE

APA

Xu, H., Sun, Q., Luo, N., Cao, G., & Xia, D. (2013). Iterative Nonlocal Total Variation Regularization Method for Image Restoration. PLoS ONE, 8(6). https://doi.org/10.1371/journal.pone.0065865

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