ICA and Genetic Algorithms for Blind Signal and Image Deconvolution and Deblurring

  • Hujun Yin
  • Israr Hussain
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


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.


Genetic Algorithm Independent Component Analysis Point Spread Function Independent Component Analysis Blind Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hujun Yin
    • 1
  • Israr Hussain
    • 1
  1. 1.School of Electrical and Electronic EngineeringThe University of ManchesterManchesterUK

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