Regularization with multilevel non-stationary tight framelets for image restoration

Yan Ran Li, Raymond H.F. Chan, Lixin Shen, Xiaosheng Zhuang

Research output: Contribution to journalLetterpeer-review

Abstract

Variational regularization models are one of the popular and efficient approaches for image restoration. The regularization functional in the model carries prior knowledge about the image to be restored. The prior knowledge, in particular for natural images, are the first-order (i.e. variance in luminance) and second-order (i.e. contrast and texture) information. In this paper, we propose a model for image restoration, using a multilevel non-stationary tight framelet system that can capture the image's first-order and second-order information. We develop an algorithm to solve the proposed model and the numerical experiments show that the model is effective and efficient as compared to other higher-order models.

Original languageEnglish (US)
Pages (from-to)332-348
Number of pages17
JournalApplied and Computational Harmonic Analysis
Volume53
DOIs
StatePublished - Jul 2021

ASJC Scopus subject areas

  • Applied Mathematics

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