@Article{JCM-41-6,
author = {Lu, Jian and Yuting, Ye and Yiqiu, Dong and Xiaoxia, Liu and Zou, Yuru},
title = {Impulse Noise Removal by L1 Weighted Nuclear Norm Minimization},
journal = {Journal of Computational Mathematics},
year = {2023},
volume = {41},
number = {6},
pages = {1171--1191},
abstract = {<p style="text-align: justify;">In recent years, the nuclear norm minimization (NNM) as a convex relaxation of the
rank minimization has attracted great research interest. By assigning different weights
to singular values, the weighted nuclear norm minimization (WNNM) has been utilized
in many applications. However, most of the work on WNNM is combined with the $l^2$-data-fidelity term, which is under additive Gaussian noise assumption. In this paper,
we introduce the L1-WNNM model, which incorporates the $l^1$-data-fidelity term and the
regularization from WNNM. We apply the alternating direction method of multipliers
(ADMM) to solve the non-convex minimization problem in this model. We exploit the low
rank prior on the patch matrices extracted based on the image non-local self-similarity and
apply the L1-WNNM model on patch matrices to restore the image corrupted by impulse
noise. Numerical results show that our method can effectively remove impulse noise.</p>},
issn = {1991-7139},
doi = {https://doi.org/10.4208/jcm.2201-m2021-0183},
url = {https://global-sci.com/article/84160/impulse-noise-removal-by-l1-weighted-nuclear-norm-minimization}
}