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http://dx.doi.org/10.3745/KTSDE.2014.3.1.43

A Genetic Programming Approach to Blind Deconvolution of Noisy Blurred Images  

Mahmood, Muhammad Tariq (한국기술교육대학교 컴퓨터공학부)
Chu, Yeon Ho (한국기술교육대학교 컴퓨터공학부)
Choi, Young Kyu (한국기술교육대학교 컴퓨터공학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.3, no.1, 2014 , pp. 43-48 More about this Journal
Abstract
Usually, image deconvolution is applied as a preprocessing step in surveillance systems to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose a blind-image deconvolution filtering approach based on genetic programming (GP). A numerical expression is developed using GP process for image restoration which optimally combines and exploits dependencies among features of the blurred image. In order to develop such function, first, a set of feature vectors is formed by considering a small neighborhood around each pixel. At second stage, the estimator is trained and developed through GP process that automatically selects and combines the useful feature information under a fitness criterion. The developed function is then applied to estimate the image pixel intensity of the degraded image. The performance of developed function is estimated using various degraded image sequences. Our comparative analysis highlights the effectiveness of the proposed filter.
Keywords
Image Restoration; Blind Deconvolution; Deblurring; Genetic Programming; Surveillance Systems;
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