Alternating direction method of multipliers for nonconvex log total variation image restoration

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

8 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)338-359
Journal / PublicationApplied Mathematical Modelling
Volume114
Online published30 Sept 2022
Publication statusPublished - Feb 2023

Abstract

In this paper, we propose a nonconvex log total variation model for image restoration. A specific alternating direction method of multipliers is also presented to solve the nonconvex optimization model. Under mild conditions, we prove that the sequence generated by the proposed alternating direction method of multipliers converges to a stationary point. Experiment results on image denoising, image deblurring, computed tomography, magnetic resonance imaging and image super-resolution demonstrate that the proposed method is effective and improves the quality of image recovery.

Research Area(s)

  • Alternating direction method of multipliers, Convergence, Image restoration, Log total variation, Nonconvex optimization

Citation Format(s)

Alternating direction method of multipliers for nonconvex log total variation image restoration. / Zhang, Benxin; Zhu, Guopu; Zhu, Zhibin et al.
In: Applied Mathematical Modelling, Vol. 114, 02.2023, p. 338-359.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review