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Title: Variational Bayesian Blind Image Deconvolution: A review

In this study, 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.
Authors:
 [1] ; ORCiD logo [2] ;  [1] ;  [1] ; ORCiD logo [3]
  1. Univ. de Granada, Granada (Spain)
  2. Beihang Univ., Beijing (China)
  3. Northwestern Univ., Evanston, IL (United States)
Publication Date:
Grant/Contract Number:
NA0002520
Type:
Accepted Manuscript
Journal Name:
Digital Signal Processing
Additional Journal Information:
Journal Volume: 47; Journal Issue: C; Journal ID: ISSN 1051-2004
Publisher:
Elsevier
Research Org:
Northwestern Univ., Evanston, IL (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Blind deconvolution; Image deblurring; Image restoration; Variational Bayesian; Bayesian modeling
OSTI Identifier:
1487825

Ruiz, Pablo, Zhou, Xu, Mateos, Javier, Molina, Rafael, and Katsaggelos, Aggelos K. Variational Bayesian Blind Image Deconvolution: A review. United States: N. p., Web. doi:10.1016/j.dsp.2015.04.012.
Ruiz, Pablo, Zhou, Xu, Mateos, Javier, Molina, Rafael, & Katsaggelos, Aggelos K. Variational Bayesian Blind Image Deconvolution: A review. United States. doi:10.1016/j.dsp.2015.04.012.
Ruiz, Pablo, Zhou, Xu, Mateos, Javier, Molina, Rafael, and Katsaggelos, Aggelos K. 2015. "Variational Bayesian Blind Image Deconvolution: A review". United States. doi:10.1016/j.dsp.2015.04.012. https://www.osti.gov/servlets/purl/1487825.
@article{osti_1487825,
title = {Variational Bayesian Blind Image Deconvolution: A review},
author = {Ruiz, Pablo and Zhou, Xu and Mateos, Javier and Molina, Rafael and Katsaggelos, Aggelos K.},
abstractNote = {In this study, 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.},
doi = {10.1016/j.dsp.2015.04.012},
journal = {Digital Signal Processing},
number = C,
volume = 47,
place = {United States},
year = {2015},
month = {5}
}