A coordinate descent method for total variation minimization
type: Item
instance of: scholarly article
title: A coordinate descent method for total variation minimization
zbMATH Open document ID: 1426.94014
DOI: 10.1155/2017/3012910
author: Yanyan Li
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 94A08
zbMATH DE Number: 6971933
Wikidata QID: Q57808617
describes a project that uses: TwIST
describes a project that uses: RecPF
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/3012910
OpenAlex ID: W2756107971
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description: scientific article; zbMATH DE number 6971933