TY - JOUR
T1 - Proximal ADMM Approach for Image Restoration with Mixed Poisson-Gaussian Noise
AU - Chen , Miao
AU - Tang , Yuchao
AU - Zhang , Jie
AU - Zeng , Tieyong
JO - Journal of Computational Mathematics
VL - 3
SP - 540
EP - 568
PY - 2024
DA - 2024/11
SN - 43
DO - http://doi.org/10.4208/jcm.2212-m2022-0122
UR - https://global-sci.org/intro/article_detail/jcm/23549.html
KW - Image restoration, Mixed Poisson-Gaussian noise, Alternating direction method of multipliers, Total variation.
AB - <p style="text-align: justify;">Image restoration based on total variation has been widely studied owing to its edge-preservation properties. In this study, we consider the total variation infimal convolution
(TV-IC) image restoration model for eliminating mixed Poisson-Gaussian noise. Based on
the alternating direction method of multipliers (ADMM), we propose a complete splitting
proximal bilinear constraint ADMM algorithm to solve the TV-IC model. We prove the
convergence of the proposed algorithm under mild conditions. In contrast with other
algorithms used for solving the TV-IC model, the proposed algorithm does not involve
any inner iterations, and each subproblem has a closed-form solution. Finally, numerical
experimental results demonstrate the efficiency and effectiveness of the proposed algorithm.</p>