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A Genetic Programming Approach to Blind Deconvolution of Noisy Blurred Images

잡음이 있고 흐릿한 영상의 블라인드 디컨벌루션을 위한 유전 프로그래밍 기법

  • ;
  • 추연호 (한국기술교육대학교 컴퓨터공학부) ;
  • 최영규 (한국기술교육대학교 컴퓨터공학부)
  • Received : 2013.07.17
  • Accepted : 2013.12.06
  • Published : 2014.01.31

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.

영상의 디컨벌루션은 보통 감시 시스템에서 모션 블러 (motion blur)나 초점이 맞지 않아 발생하는 블러 (out-of-focus blur) 문제를 줄이기 위해 전처리 과정에서 사용된다. 본 논문에서는 유전 프로그래밍 (Genetic Programming, GP)에 기반한 새로운 블라인드 영상 디컨벌루션 필터링 방법을 제안한다. GP 진화 과정을 통해 영상 복원을 위한 수학적 표현이 만들어지는데, 이것은 블러 영상의 특징들 사이의 관계를 최적으로 조합하여 원래 화소 값을 복원할 수 있는 추정자 함수가 된다. 이를 위해, 먼저 각 화소의 작은 이웃으로부터 특징 벡터를 만들고 추정자 함수를 학습시키는데, 이러한 GP 진화 과정을 통해 지정한 적합성 기준에 따라 유용한 정보들이 자동으로 조합된다. 개발된 함수는 훼손된 영상의 각 화소에 적용하여 원래의 화소 값을 복원하게 된다. 개발된 함수는 다양한 방법으로 훼손된 영상에 적용하여 실험하였는데, 실험 결과 제안된 방법이 기존 알고리즘에 비해 좋은 결과를 나타내는 것을 알 수 있었다.

Keywords

References

  1. D. Vallejo, "A multi-agent architecture for supporting distributed normality-based intelligent surveillance," Engineering Applications of Artificial Intelligence, vol. 23, no. 2, pp. 325-340, 2011.
  2. M.T. Mahmood and T.S. Choi, "Optimal depth estimation by combining focus measures using genetic programming," Information Sciences, vol. 181, pp. 1249-1263, 2011.. https://doi.org/10.1016/j.ins.2010.11.039
  3. D. Li, R.M. Mersereau and S. Simske, "Blind Image Deconvolution Through Support Vector Regression," IEEE Transactions on Neural Networks, vol. 18, no. 3, pp. 931-935, 2007. https://doi.org/10.1109/TNN.2007.891622
  4. W.H. Richardson, "Bayesian-Based Iterative Method of Image Restoration," J. Opt. Soc. Am., vol. 62, no. 1, pp. 55-59, 1972. https://doi.org/10.1364/JOSA.62.000055
  5. M. Grimble, "Weiner and Kalman filters for systems with random parameters," IEEE Transactions on Automatic Control, vol. 29, no. 6, pp. 552-554. 1984. https://doi.org/10.1109/TAC.1984.1103581
  6. E. Besdok, "A new method for impulsive noise suppression from highly distorted images by using Anfis," Engineering Applications of Artificial Intelligence, vol. 17, no. 5, pp. 519-527, 2004. https://doi.org/10.1016/j.engappai.2004.03.009
  7. S.C. Tan, C.P. Lim and M.V. Rao, "A hybrid neural network model for rule generation and its application to process fault detection and diagnosis," Engineering Applications of Artificial Intelligence, vol. 20, no. 2, pp. 203-213. 2007. https://doi.org/10.1016/j.engappai.2006.06.007
  8. S.M. Jakubek and T.I. Strasser, "Artificial neural networks for fault detection in large-scale data acquisition systems," Engineering Applications of Artificial Intelligence, vol. 17, no. 3, pp. 233-248, 2004. https://doi.org/10.1016/j.engappai.2004.03.002
  9. M. Fahmy, G. Raheem1, U. Mohamed, and O. Fahmy, "A New Fast Iterative Blind Deconvolution Algorithm," Journal of Signal and Information Processing, vol. 3, pp. 98-108, 2012 https://doi.org/10.4236/jsip.2012.31013
  10. N.I. Petrovic and V. Crnojevic, "Universal Impulse Noise Filter Based on Genetic Programming," IEEE Transactions on Image Processing, vol. 17, no. 7, pp. 1109-1120. 2008. https://doi.org/10.1109/TIP.2008.924388
  11. A. Majid, M.T. Mahmood and T.S. Choi, "Optimal Composite Depth Function for 3D Shape Recovery of Microscopic Objects," Microscopy Research and Technique, vol. 73, pp. 657-661, 2010.
  12. A. Majid, A, Khan and A.M. Mirza, "Combination of support vector machines using genetic programming," International Journal of Hybrid Intelligent Systems, vol. 3, no. 2, pp. 109-125. 2006.
  13. J.R. Koza, M.J. Streeter and M.A. Keane, "Routine highreturn human-competitive automated problem-solving by means of genetic programming," Information Sciences, vol. 178, no. 23, pp. 4434-4452. 2008. https://doi.org/10.1016/j.ins.2008.07.028
  14. S. Silva and J. Almeida, "GPLAB-a genetic programming toolbox for MATLAB," 2003.