Blind space-variant single-image restoration of defocus blur

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We address the problem of blind piecewise space-variant image deblurring where only part of the image is sharp, assuming a shallow depth of field which imposes significant defocus blur. We propose an automatic image recovery approach which segments the sharp and blurred sub-regions, iteratively estimates a non-parametric blur kernel and restores the sharp image via a variational non-blind space variant method. We present a simple and efficient blur measure which emphasizes the blur difference of the sub-regions followed by a blur segmentation procedure based on an evolving level set function. One of the contributions of this work is the extension to the space-variant case of progressive blind deconvolution recently proposed, an iterative process consisting of non-parametric blind kernel estimation and residual blur deblurring. Apparently this progressive strategy is superior to the one step deconvolution procedure. Experimental results on real images demonstrate the effectiveness of the proposed algorithm.

Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision - 6th International Conference, SSVM 2017, Proceedings
EditorsFrancois Lauze, Yiqiu Dong, Anders Bjorholm Dahl
PublisherSpringer Verlag
Pages109-120
Number of pages12
ISBN (Print)9783319587707
DOIs
StatePublished - 2017
Event6th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2017 - Kolding, Denmark
Duration: 4 Jun 20178 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10302 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2017
Country/TerritoryDenmark
CityKolding
Period4/06/178/06/17

Keywords

  • Blind deconvolution
  • Blur segmentation
  • Space-variant deblurring

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