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Volume 16, Issue 4
Low Rank and Total Variation Based Two-Phase Method for Image Deblurring with Salt-and-Pepper Impulse Noise

Yuchao Tang, Shirong Deng & Tieyong Zeng

Numer. Math. Theor. Meth. Appl., 16 (2023), pp. 1013-1034.

Published online: 2023-11

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  • Abstract

Although there are many effective methods for removing impulse noise in image restoration, there is still much room for improvement. In this paper, we propose a new two-phase method for solving such a problem, which combines the nuclear norm and the total variation regularization with box constraint. The popular alternating direction method of multipliers and the proximal alternating direction method of multipliers are employed to solve this problem. Compared with other algorithms, the obtained algorithm has an explicit solution at each step. Numerical experiments demonstrate that the proposed method performs better than the state-of-the-art methods in terms of both subjective and objective evaluations.

  • AMS Subject Headings

65K05, 90C25, 94A08

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{NMTMA-16-1013, author = {Tang , YuchaoDeng , Shirong and Zeng , Tieyong}, title = {Low Rank and Total Variation Based Two-Phase Method for Image Deblurring with Salt-and-Pepper Impulse Noise}, journal = {Numerical Mathematics: Theory, Methods and Applications}, year = {2023}, volume = {16}, number = {4}, pages = {1013--1034}, abstract = {

Although there are many effective methods for removing impulse noise in image restoration, there is still much room for improvement. In this paper, we propose a new two-phase method for solving such a problem, which combines the nuclear norm and the total variation regularization with box constraint. The popular alternating direction method of multipliers and the proximal alternating direction method of multipliers are employed to solve this problem. Compared with other algorithms, the obtained algorithm has an explicit solution at each step. Numerical experiments demonstrate that the proposed method performs better than the state-of-the-art methods in terms of both subjective and objective evaluations.

}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.OA-2022-0190}, url = {http://global-sci.org/intro/article_detail/nmtma/22121.html} }
TY - JOUR T1 - Low Rank and Total Variation Based Two-Phase Method for Image Deblurring with Salt-and-Pepper Impulse Noise AU - Tang , Yuchao AU - Deng , Shirong AU - Zeng , Tieyong JO - Numerical Mathematics: Theory, Methods and Applications VL - 4 SP - 1013 EP - 1034 PY - 2023 DA - 2023/11 SN - 16 DO - http://doi.org/10.4208/nmtma.OA-2022-0190 UR - https://global-sci.org/intro/article_detail/nmtma/22121.html KW - Image deblurring, impulse noise, total variation, nuclear norm. AB -

Although there are many effective methods for removing impulse noise in image restoration, there is still much room for improvement. In this paper, we propose a new two-phase method for solving such a problem, which combines the nuclear norm and the total variation regularization with box constraint. The popular alternating direction method of multipliers and the proximal alternating direction method of multipliers are employed to solve this problem. Compared with other algorithms, the obtained algorithm has an explicit solution at each step. Numerical experiments demonstrate that the proposed method performs better than the state-of-the-art methods in terms of both subjective and objective evaluations.

Tang , YuchaoDeng , Shirong and Zeng , Tieyong. (2023). Low Rank and Total Variation Based Two-Phase Method for Image Deblurring with Salt-and-Pepper Impulse Noise. Numerical Mathematics: Theory, Methods and Applications. 16 (4). 1013-1034. doi:10.4208/nmtma.OA-2022-0190
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