Accelerated alternating minimization algorithm for Poisson noisy image recovery. Issue 7 (2nd July 2020)

• Record Type: Journal Article
• Authors: Padcharoen, Anantachai; Kitkuan, Duangkamon; Kumam, Poom; Rilwan, Jewaidu; Kumam, Wiyada
• Is Part Of: Inverse problems in science and engineering. Volume 28:Issue 7(2020)
• Page Start: 1031
• Page End: 1056
• Publication Date: 2020-07-02
• Subjects: Total variation (TV) -- image restoration -- alternating direction method -- correction procedure -- Poisson noise images; 47A52 -- 47H20 -- 49M20 -- 49M25 -- 49M27 -- 53D12 -- 47J25 -- 47H05; Engineering mathematics -- Periodicals; Inverse problems (Differential equations) -- Periodicals; 620.001515357
• DOI: 10.1080/17415977.2019.1709454
• Languages: English
• ISSNs: 1741-5977

Abstract

Abstract : Restoring images corrupted by Poisson noise have attracted much attention in recent years due to its significant applications in image processing. There are various regularization methods of solving this problem and one of the most famous is the total variation (TV) model. In this paper, we present a new method based on accelerated alternating minimization algorithm (AAMA) which involves minimizing the sum of a Kullback–Leibler divergence term and a TV term for restoring Poisson noise degraded images. Our proposed algorithm is applied in solving the aforementioned problem and its convergence analysis is established under very weak conditions. In addition, the numerical examples reported demonstrate the efficiency and versatility of our method compared to existing methods of restoring images with Poisson noise.