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Proximal ADMM Approach for Image Restoration with Mixed Poisson-Gaussian Noise

Proximal ADMM Approach for Image Restoration with Mixed Poisson-Gaussian Noise

Year:    2025

Author:    Miao Chen, Yuchao Tang, Jie Zhang, Tieyong Zeng

Journal of Computational Mathematics, Vol. 43 (2025), Iss. 3 : pp. 540–568

Abstract

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.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/jcm.2212-m2022-0122

Journal of Computational Mathematics, Vol. 43 (2025), Iss. 3 : pp. 540–568

Published online:    2025-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    29

Keywords:    Image restoration Mixed Poisson-Gaussian noise Alternating direction method of multipliers Total variation.

Author Details

Miao Chen

Yuchao Tang

Jie Zhang

Tieyong Zeng