Applying smoothing technique and semi-proximal ADMM for image deblurring

Caiying Wu, Xiaojuan Chen, Qiyu Jin, Jein Shan Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We present a new approach which combines smoothing technique and semi-proximal alternating direction method of multipliers for image deblurring. More specifically, in light of a nondifferentiable model, which is indeed of the hybrid model of total variation and Tikhonov regularization models, we consider a smoothing approximation to conquer the disadvantage of nonsmoothness. We employ four smoothing functions to approximate the hybrid model and build up a new model accordingly. It is then solved by semi-proximal alternating direction method of multipliers. The algorithm is shown globally convergent. Numerical experiments and comparisons affirm that our method is an efficient approach for image deblurring.

Original languageEnglish
Article number40
JournalCalcolo
Volume59
Issue number4
DOIs
Publication statusPublished - 2022 Nov

Keywords

  • Image restoration
  • SP-ADMM
  • Smoothing function
  • TV regularization

ASJC Scopus subject areas

  • Algebra and Number Theory
  • Computational Mathematics

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