Genetic programming based blind image deconvolution for surveillancesystems

Authors:
Muhammad Tariq Mahmood;Abdul Majid;Jongwoo Han;Young Kyu Choi
Affiliations:
School of Computer Science and Engineering, Korea University of Education and Technology, Republic of Korea;Department of Information and Computer Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan;School of Computer Science and Engineering, Korea University of Education and Technology, Republic of Korea;School of Computer Science and Engineering, Korea University of Education and Technology, Republic of Korea
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

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Abstract

Image acquisition, segmentation, object detection and tracking are essential parts of surveillance systems. Usually, image filtering approaches are employed as preprocessing step to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose genetic programming (GP) based blind-image deconvolution filter. A GP based numerical expression is developed 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 images. The performance of filter function is estimated using various degraded image sequences. Our comparative analysis highlight the effectiveness of GP based proposed filter.