Variational Bayesian Blind Image Deconvolution: A review

In this paper we provide a review of the recent literature on Bayesian Blind Image Deconvolution (BID) methods. We believe that two events have marked the recent history of BID: the predominance of Variational Bayes (VB) inference as a tool to solve BID problems and the increasing interest of the co...

Full description

Saved in:
Bibliographic Details
Published in:Digital signal processing 2015-12, Vol.47 (C), p.116-127
Main Authors: Ruiz, Pablo, Zhou, Xu, Mateos, Javier, Molina, Rafael, Katsaggelos, Aggelos K.
Format: Article
Language:English
Subjects:
Bayesian modeling
Blind deconvolution
Image deblurring
Image restoration
MATHEMATICS AND COMPUTING
Variational Bayesian
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper we provide a review of the recent literature on Bayesian Blind Image Deconvolution (BID) methods. We believe that two events have marked the recent history of BID: the predominance of Variational Bayes (VB) inference as a tool to solve BID problems and the increasing interest of the computer vision community in solving BID problems. VB inference in combination with recent image models like the ones based on Super Gaussian (SG) and Scale Mixture of Gaussians (SMG) representations have led to the use of very general and powerful tools to provide clear images from blurry observations. In the provided review emphasis is paid on VB inference and the use of SG and SMG models with coverage of recent advances in sampling methods. We also provide examples of current state of the art BID methods and discuss problems that very likely will mark the near future of BID.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2015.04.012