An Inexact Dual Alternating Direction Method of Multipliers for Image Decomposition and Restoration

Qingsong Wang, Chengjing Wang, Peipei Tang () and Dunbiao Niu ()
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Qingsong Wang: School of Mathematics, Southwest Jiaotong University, No. 999, Xian Road, West Park, High-tech Zone, Chengdu 611756, China2School of Mathematical Sciences, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China
Chengjing Wang: School of Mathematics and National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, No. 999, Xian Road, West Park, High-tech Zone, Chengdu 611756, China
Peipei Tang: School of Computer and Computing Science, Zhejiang University City College, Hangzhou 310015, China
Dunbiao Niu: College of Mathematics, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, China

Asia-Pacific Journal of Operational Research (APJOR), 2020, vol. 37, issue 04, 1-25

Abstract: In this paper, we apply an inexact dual alternating direction method of multipliers (dADMM) to an image decomposition model. In this model, an image is divided into two meaningful components, i.e., a cartoon part and a texture part. The dADMM presents not only the cartoon part and the texture part of an image but also the restored image (cartoon part + texture part). We also summarize the global convergence for the algorithm. Numerical experiments demonstrate the efficiency and robustness of the inexact dADMM. Furthermore, we can obtain relatively higher signal-to-noise ratio (SNR) comparing to other algorithms.

Keywords: Cartoon and texture; deblurring; image decomposition; inexact alternating direction method of multipliers; inpainting; total variation (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0217595920400102

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