A finite Markov Random Field approach to fast edge-preserving image recovery

被引：12

作者：

Ceccarelli, Michele ^{[1
]}

机构：

[1] Univ Sannio, RCOST, I-82100 Benevento, Italy

来源：

IMAGE AND VISION COMPUTING | 2007年 / 25卷 / 06期

关键词：

Markov random fields; image denoising; edge-preserving potentials;

D O I：

10.1016/j.imavis.2006.05.021

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We investigate the properties of edge-preserving smoothing in the context of Finite Markov Random Fields (FMRF). Our main result follows from the definition of discontinuity adaptive potential for FMRF which imposes to penalize linearly image gradients. This is in agreement with the Total Variation based regularization approach to image recovery and analysis. We also report a fast computational algorithm exploiting the finiteness of the field, it uses integer arithmetic and a gradient descent updating procedure. Numerical results on real images and comparisons with anisotropic diffusion and half-quadratic regularization are reported. (C) 2006 Elsevier B.V. All rights reserved.

引用

页码：792 / 804

页数：13

共 26 条

[1] SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling [J].