A plug and play Bayesian algorithm for solving myope inverse problems

Lotfi Chaari, Jean-Yves Tourneret, Hadj Batatia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The emergence of efficient algorithms in variational and Bayesian frameworks braught significant advances to the field of inverse problems. However, such problems remain challenging when the observation operator is not perfectly known. In this paper we propose a Bayesian Plug-and-Play (PP) algorithm for solving a wide range of inverse problems where the signal/image is sparse in the original domain and the observation operator has to be estimated. The principle consists of plugging the prior considered for the target observation operator and keep using the same algorithm. The proposed method relies on a generic proximal non-smooth sampling scheme. This genericity makes the proposed algorithm novel in the sense that it can be used to solve a wide range or inverse problems. Our method is illustrated on a deblurring problem with unknown blur operator where promising results are obtained.

Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages737-741
Number of pages5
ISBN (Electronic)9789082797015
DOIs
Publication statusPublished - 3 Dec 2018
Event26th European Signal Processing Conference 2018 - Rome, Italy
Duration: 3 Sep 20187 Sep 2018

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491
ISSN (Electronic)2076-1465

Conference

Conference26th European Signal Processing Conference 2018
Abbreviated titleEUSIPCO 2018
Country/TerritoryItaly
CityRome
Period3/09/187/09/18

Keywords

  • MCMC
  • Myope inverse problems
  • Ns-HMC
  • Proximity operator

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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