Regularization with multilevel non-stationary tight framelets for image restoration

被引：4

作者：

Li, Yan-Ran ^{[1
,2
,3
,4
]}

Chan, Raymond H. F. ^{[5
]}

Shen, Lixin ^{[6
]}

Zhuang, Xiaosheng ^{[5
]}

机构：

[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China

[2] Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Peoples R China

[3] Shenzhen Univ, Shenzhen Key Lab Media Secur, Shenzhen 518060, Peoples R China

[4] Shenzhen Inst Artificial Intelligence & Robot Soc, SZU Branch, Shenzhen, Peoples R China

[5] City Univ Hong Kong, Dept Math, Kowloon Tong, Tat Chee Ave, Hong Kong, Peoples R China

[6] Syracuse Univ, Dept Math, Syracuse, NY 13244 USA

来源：

APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS | 2021年 / 53卷

基金：

美国国家科学基金会;

关键词：

NOISE REMOVAL; RECOVERY; ALGORITHMS;

D O I：

10.1016/j.acha.2021.03.003

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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. (C) 2021 Elsevier Inc. All rights reserved.

引用

页码：332 / 348

页数：17

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