A PROJECTED GRADIENT ALGORITHM FOR IMAGE RESTORATION UNDER HESSIAN MATRIX-NORM REGULARIZATION

被引：0

作者：

Lefkimmiatis, Stamatios ^{[1
]}

Unser, Michael ^{[1
]}

机构：

[1] Ecole Polytech Fed Lausanne, Biomed Imaging Grp, CH-1015 Lausanne, Switzerland

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2012) | 2012年

关键词：

Linear inverse problems; image restoration; Hessian matrix norms; mixed-norm regularization; THRESHOLDING ALGORITHM; MINIMIZATION;

D O I：

暂无

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

We have recently introduced a class of non-quadratic Hessian-based regularizers as a higher-order extension of the total variation (TV) functional. These regularizers retain some of the most favorable properties of TV while they can effectively deal with the staircase effect that is commonly met in TV-based reconstructions. In this work we propose a novel gradient-based algorithm for the efficient minimization of these functionals under convex constraints. Furthermore, we validate the overall proposed regularization framework for the problem of image deblurring under additive Gaussian noise.

引用

页码：3029 / 3032

页数：4

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