JOURNAL ARTICLE

Iterative Nonlocal Total Variation Regularization Method for Image Restoration

Huanyu XuQuansen SunNan LuoGuo CaoXia De-shen

Year: 2013 Journal:   PLoS ONE Vol: 8 (6)Pages: e65865-e65865   Publisher: Public Library of Science

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.

Keywords:
Total variation denoising Regularization (linguistics) Image restoration Variation (astronomy) Algorithm Mathematics Iterative method Mathematical optimization Image (mathematics) Computer science Image processing Artificial intelligence Physics

Metrics

13
Cited By
1.04
FWCI (Field Weighted Citation Impact)
42
Refs
0.80
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Image and Signal Denoising Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image Processing Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Sparse and Compressive Sensing Techniques
Physical Sciences →  Engineering →  Computational Mechanics

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