Blind deconvolution of images corrupted by Gaussian noise using Expectation Propagation

Abdullah Abdulaziz, Dan Yao, Yoann Altmann, Stephen McLaughlin

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

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Abstract

Blind image deconvolution consists of inferring an image from its blurry and noisy version when the blur is unknown.
To solve this highly ill-posed inverse problem, Expectation Maximization (EM)-based algorithms can be adopted.
In several previous studies, Variational Bayes (VB) approaches were deployed to approximate the intractable conditional probability distribution of the image that appears in the E-step of the traditional EM algorithm.
In this paper, we propose to use an Expectation Propagation (EP) algorithm to derive an alternative approximation of the conditional probability distribution.
The simulations conducted show that the resulting EP-EM approach can provide more reliable approximations, reflected by better image estimates and more reliable uncertainty maps than VB-EM for a comparable computational time.
Original languageEnglish
Title of host publication29th European Signal Processing Conference, EUSIPCO 2021
Publication statusAccepted/In press - 4 May 2021
Event29th European Signal Processing Conference 2021 - Virtual, Dublin, Ireland
Duration: 23 Aug 202127 Aug 2021

Conference

Conference29th European Signal Processing Conference 2021
Abbreviated titleEUSIPCO 2021
Country/TerritoryIreland
CityDublin
Period23/08/2127/08/21

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