Bayesian inference for inverse problems in signal and image processing and applications

被引：13

作者：

Mohammad-Djafari, Ali ^{[1
]}

机构：

[1] UPS, CNRS,Unite Mixte Rech 8506, Supelec, Signaux & Syst Lab, F-91192 Gif Sur Yvette, France

来源：

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY | 2006年 / 16卷 / 05期

关键词：

inverse problems; Bayesian estimation; deconvolution; image restoration; computed tomography; blind source separation; data and image fusion;

D O I：

10.1002/ima.20081

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Probability theory and statistics are two main tools in signal and image processing. Bayesian inference has a privileged place in developing methods for inverse problems arising in signal and image processing, which can be applied in real world applications. In this tutorial presentation, first I will briefly present the Bayesian estimation approach in signal and image processing. Then, I will show a few examples of inverse problems, such as signal deconvolution, image restoration, and tomographic image construction, and then show how the Bayesian estimation approach can be used to give solutions for these problems. Finally, I will focus on two recent research domain, which are blind sources separation and data fusion problems, and present new methods we developed recently and their applications. (C) 2007 Wiley Periodicals, Inc.

引用

页码：209 / 214

页数：6