A Unified Framework for the Restoration of Images Corrupted by Additive White Noise

被引：2

作者：

Lanza, Alessandro ^{[1
]}

Sciacchitano, Federica ^{[2
]}

Morigi, Serena ^{[1
]}

Sgallari, Fiorella ^{[1
]}

机构：

[1] Univ Bologna, Dept Math, Bologna, Italy

[2] Univ Genoa, Dept Math, Genoa, Italy

来源：

SCALE SPACE AND VARIATIONAL METHODS IN COMPUTER VISION, SSVM 2017 | 2017年 / 10302卷

关键词：

Variational image restoration; Additive white noise; Total Variation; Non-convex non-smooth optimization; ADMM; VARIATIONAL APPROACH; REGULARIZATION; MODEL;

D O I：

10.1007/978-3-319-58771-4_40

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We propose a robust variational model for the restoration of images corrupted by blur and the general class of additive white noises. The solution of the non-trivial optimization problem, due to the non-smooth non-convex proposed model, is efficiently obtained by an Alternating Directions Method of Multipliers (ADMM), which in particular reduces the solution to a sequence of convex optimization sub-problems. Numerical results show the potentiality of the proposed model for restoring blurred images corrupted by several kinds of additive white noises.

引用

页码：498 / 510

页数：13

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