ICA and genetic algorithms for blind signal and image deconvolution and deblurring

Hujun Yin, Israr Hussain

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

    Abstract

    Signals and images often suffer from blurring or point spreading with unknown filter or point spread function. Most existing blind deconvolution and deblurring methods require good knowledge about both the signal and the filter and the performance depends on the amount of prior information regarding the blurring function and signal. Often an iterative procedure is required for estimating the blurring function such as the Richardson-Lucy method and is computational complex and expensive and sometime unstable. In this paper a blind signal deconvolution and deblurring method is proposed based on an ICA measure as well as a simple genetic algorithm. The method is simple and does not require any priori knowledge regarding the signal and the blurring function. Experimental results are presented and compared with some existing methods. © Springer-Verlag Berlin Heidelberg 2006.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    PublisherSpringer Nature
    Pages595-603
    Number of pages8
    Volume4224
    ISBN (Print)3540454853, 9783540454854
    Publication statusPublished - 2006
    Event7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 - Burgos
    Duration: 1 Jul 2006 → …

    Conference

    Conference7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006
    CityBurgos
    Period1/07/06 → …

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