A Genetic Programming Approach to Blind Deconvolution of Noisy Blurred Images


KIPS Transactions on Software and Data Engineering, Vol. 3, No. 1, pp. 43-48, Jan. 2014
10.3745/KTSDE.2014.3.1.43, Full Text:

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.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
M. T. Mahmood†? and Y. H. Chu, "A Genetic Programming Approach to Blind Deconvolution of Noisy Blurred Images," KIPS Transactions on Software and Data Engineering, vol. 3, no. 1, pp. 43-48, 2014. DOI: 10.3745/KTSDE.2014.3.1.43.

[ACM Style]
Muhammad Tariq Mahmood†? and Yeon Ho Chu. 2014. A Genetic Programming Approach to Blind Deconvolution of Noisy Blurred Images. KIPS Transactions on Software and Data Engineering, 3, 1, (2014), 43-48. DOI: 10.3745/KTSDE.2014.3.1.43.