@Article{AAMM-6-2,
author = {Zhu, Wei and Shu, Shi and Lizhi, Cheng},
title = {An Efficient Proximity Point Algorithm for Total-Variation-Based Image Restoration},
journal = {Advances in Applied Mathematics and Mechanics},
year = {2014},
volume = {6},
number = {2},
pages = {145--164},
abstract = {<p style="text-align: justify;">In this paper, we propose a fast proximity point algorithm and apply it to total
 variation (TV) based image restoration. The novel method is derived from the idea
 of establishing a general proximity point operator framework based on which new
 first-order schemes for total variation (TV) based image restoration have been proposed.
 Many current algorithms for TV-based image restoration, such as Chambolle&#39;s projection
 algorithm, the split Bregman algorithm, the Bermúdez-Moreno algorithm, the Jia-Zhao
 denoising algorithm, and the fixed point algorithm, can be viewed as special cases of the
 new first-order schemes. Moreover, the convergence of the new algorithm has been analyzed
 at length. Finally, we make comparisons with the split Bregman algorithm which is one of
 the best algorithms for solving TV-based image restoration at present. Numerical
 experiments illustrate the efficiency of the proposed algorithms.</p>},
issn = {2075-1354},
doi = {https://doi.org/10.4208/aamm.2013.m175},
url = {https://global-sci.com/article/73434/an-efficient-proximity-point-algorithm-for-total-variation-based-image-restoration}
}