Bayesian partial out-of-focus blur removal with parameter estimation

Bruno Amizic*, Rafael Molina, Aggelos K Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper we propose a novel partial out-of-focus blur removal method developed within the Bayesian framework. We concentrate on the removal of background out-of-focus blurs that are present in the images in which there is a strong interest to keep the foreground in sharp focus. However, often there is a desire to recover background details out of such partially blurred image. In this work, a non-convex l p-norm prior with 0 < p < 1 is used as the background and foreground image prior and a total variation (TV) based prior is utilized for both the background blur and the occlusion mask, that is, the mask determining the pixels belonging to the foreground. In order to model transparent foregrounds, the values in the occlusion mask are assumed to belong to the closed interval [0,1]. The proposed method is derived by utilizing bounds on the priors for the background and foreground image, the background blur and the occlusion mask using the majorization-minimization principle. Maximum a posteriori Bayesian inference is performed and as a result, the background and foreground image, the background blur, the occlusion mask and the model parameters are simultaneously estimated. Experimental results are presented to demonstrate the advantage of the proposed method over the existing ones.

Original languageEnglish (US)
Pages (from-to)1673-1677
Number of pages5
JournalEuropean Signal Processing Conference
StatePublished - Dec 1 2011
Event19th European Signal Processing Conference, EUSIPCO 2011 - Barcelona, Spain
Duration: Aug 29 2011Sep 2 2011

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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