Variational Bayesian blind image deconvolution: A review

Pablo Ruiz, Xu Zhou*, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalReview article

44 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)116-127
Number of pages12
JournalDigital Signal Processing: A Review Journal
Volume47
DOIs
StatePublished - Dec 2015

Keywords

  • Bayesian modeling
  • Blind deconvolution
  • Image deblurring
  • Image restoration
  • Variational Bayesian

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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