Accelerated alternating minimization algorithm for Poisson noisy image recovery

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...

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Bibliographic Details
Published in:Inverse problems in science and engineering Vol. 28; no. 7; pp. 1031 - 1056
Main Authors: Padcharoen, Anantachai, Kitkuan, Duangkamon, Kumam, Poom, Rilwan, Jewaidu, Kumam, Wiyada
Format: Journal Article
Language:English
Published: Taylor & Francis 02.07.2020
Subjects:
alternating direction method
correction procedure
image restoration
Poisson noise images
Total variation (TV)
ISSN:1741-5977, 1741-5985
Online Access:Get full text
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Summary: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.
ISSN:1741-5977
1741-5985
DOI:10.1080/17415977.2019.1709454