@Article{NMTMA-16-4,
author = {Shirong, Deng and Zeng, Tieyong and Yuchao, Tang and Shirong, Deng 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 = {<p style="text-align: justify;">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.</p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.OA-2022-0190},
url = {https://global-sci.com/article/90237/low-rank-and-total-variation-based-two-phase-method-for-image-deblurring-with-salt-and-pepper-impulse-noise}
}