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dc.contributor.authorDash, R-
dc.contributor.authorMajhi, B-
dc.identifier.citation2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009; Coimbatore; 9 December 2009 through 11 December 2009; Category number CFP0995H; Code 79534; Article number 5393754, Pages 1253-1257en
dc.description.abstractImage restoration from a degraded observation has been a long standing problem in image processing. It requires a direct inversion of the degradation function in frequency domain which is ill posed in nature. So regularization has been used in the restoration process. The selection of regularization parameter still remains a difficult problem due to the amplification of noise in the inversion process. In this paper, we have proposed a PSO based regularization technique which adapts the regularization parameters depending on the noise and blurring conditions in the degraded image. Experimental results are presented to validate the efficiency of the proposed scheme.en
dc.format.extent1127344 bytes-
dc.subjectDegraded images;en
dc.subjectDirect inversion;en
dc.subjectFrequency domains;en
dc.subjectIll posed;en
dc.subjectImage restoration;en
dc.subjectInversion process;en
dc.subjectRegularization parameters;en
dc.subjectRegularization technique;en
dc.subjectRestoration process;en
dc.subjectStanding problemsen
dc.titleParticle Swarm Optimization Based Regularization for Image Restorationen
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