Orthogonality based stopping condition for iterative image deconvolution methods

Dániel Szolgay, Tamás Szirányi

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

Abstract

Deconvolution techniques are widely used for image enhancement from microscopy to astronomy. The most effective methods are based on some iteration techniques, including Bayesian blind methods or Greedy algorithms. The stopping condition is a main issue for all the non-regularized methods, since practically the original image is not known, and the estimation of quality is based on some distance between the measured image and its estimated counter-part. This distance is usually the mean square error (MSE), driving to an optimization on the Least-Squares measure. Based on the independence of signal and noise, we have established a new type of error measure, checking the orthogonality criterion of the measurement driven gradient and the estimation at a given iteration. We give an automatic procedure for estimating the stopping condition. We show here its superiority against conventional ad-hoc non-regularized methods at a wide range of noise models.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
Pages321-332
Number of pages12
EditionPART 4
DOIs
Publication statusPublished - Mar 16 2011
Event10th Asian Conference on Computer Vision, ACCV 2010 - Queenstown, New Zealand
Duration: Nov 8 2010Nov 12 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume6495 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th Asian Conference on Computer Vision, ACCV 2010
CountryNew Zealand
CityQueenstown
Period11/8/1011/12/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Szolgay, D., & Szirányi, T. (2011). Orthogonality based stopping condition for iterative image deconvolution methods. In Computer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers (PART 4 ed., pp. 321-332). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6495 LNCS, No. PART 4). https://doi.org/10.1007/978-3-642-19282-1_26