Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74765
Title: A coordinate descent method for total variation minimization
Authors: Deng, H
Ren, D
Xiao, G
Zhang, D 
Zuo, W
Issue Date: 2017
Publisher: Hindawi Limited
Source: Mathematical problems in engineering, 2017, v. 2017, 3012910 How to cite?
Journal: Mathematical problems in engineering 
Abstract: Total variation (TV) is a well-known image model with extensive applications in various images and vision tasks, for example, denoising, deblurring, superresolution, inpainting, and compressed sensing. In this paper, we systematically study the coordinate descent (CoD) method for solving general total variation (TV) minimization problems. Based on multidirectional gradients representation, the proposed CoD method provides a unified solution for both anisotropic and isotropic TV-based denoising (CoDenoise). With sequential sweeping and small random perturbations, CoDenoise is efficient in denoising and empirically converges to optimal solution. Moreover, CoDenoise also delivers new perspective on understanding recursive weighted median filtering. By incorporating with the Augmented Lagrangian Method (ALM), CoD was further extended to TV-based image deblurring (ALMCD). The results on denoising and deblurring validate the efficiency and effectiveness of the CoD-based methods.
URI: http://hdl.handle.net/10397/74765
ISSN: 1024-123X
EISSN: 1563-5147
DOI: 10.1155/2017/3012910
Rights: Copyright © 2017 Hong Deng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following article: Hong Deng, Dongwei Ren, Gang Xiao, David Zhang, and Wangmeng Zuo, “A Coordinate Descent Method for Total Variation Minimization,” Mathematical Problems in Engineering, vol. 2017, Article ID 3012910, 13 pages, 2017 is available at https://doi.org/10.1155/2017/3012910.
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