Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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A Modified Adaptive Switching Median Filter for Image Restoration

영상복원(映像復原)을 위한 변형(變形)된 적응(適應) 스위칭 메디안 필터

  • ;
  • 김남호 (부경대학교 전기제어공학부)
  • Published : 2007.07.31
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Abstract

A modified adaptive switching median filter for impulse noise removal, which has the noise detection step and the noise filtering step, is proposed in this paper. In the noise detection step, we use the detection threshold which is earned by calculating the intensity differences between pixels nearby with each other in localized window, to determine whether the pixels in the image are noise or not. Then in the noise filtering step, we will only remove the corrupted pixels and remain the good pixels. By the noise detection result, we can easily get the local noise density of the image, and use it to consider the filtering mask size and the times of filtering iteration according to different localized noise corruptions. For Setting the simulation result, we compared the proposed method to conventional median filters with several test images corrupted by various impulse noise densities. We also use the peak signal-to-noise ratio (PSNR) to evaluate restoration performance, the simulation results demonstrate that the proposed method shows better results than other median-based type filters.

본 논문에서는 임펄스 잡음검출과 필터링을 기반으로 하는 변형된 적응 스위칭 메디안 필터 알고리즘을 제안하였다. 임펄스 잡음검출 과정에서는 마스크 내의 화소값의 차에 의해 설정된 임계값을 사용하며, 이 때 잡음으로 판단된 화소들에 대하여 필터링 과정을 수행한다. 제안한 필터링 방법은 국부 잡음밀도에 상응하여 마스크 크기를 가변하며, 필터링을 반복 수행한다. 그리고 제안한 방법의 시뮬레이션을 위해, 다양한 밀도의 임펄스 잡음을 원영상에 중첩하여 테스트 영상으로 사용하였으며, 기존의 방법과 비교하였다. 또한 평가를 위한 기준으로 PSNR을 적용하였으며, 시뮬레이션 결과에서 제안한 알고리즘은 우수한 성능을 나타내었다.

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References

  1. I. Pitas and A. N. Venetsanopoulos, 'Order statistics in digital image processing', in Proc. IEEE, vol. 80, no. 12, pp. 1893-1921, Dec. 1992 https://doi.org/10.1109/5.192071
  2. D. R. K. Brownrigg, 'The weighted median filter', Commun. ACM, vol. 27, no. 8, pp. 807-818, Aug. 1984 https://doi.org/10.1145/358198.358222
  3. S. J. Ko and Y. H. Lee, 'Center weighted median filters and applications to image enhancement', IEEE Trans. Circuits Syst., vol. 38, no. 9, pp. 984-993, Sep. 1991 https://doi.org/10.1109/31.83870
  4. H. Kong and L. Wang, 'A Neural Network Adaptive Filter for the Removal of Impulse Noise in Digital Images', Neural Networks, vol. 9, pp. 373-378, 1996 https://doi.org/10.1016/0893-6080(95)00128-X
  5. Z. Wang and D. Zhang, 'Progressive Switching Median Filter for the Removal of Impulse Noise from Highly Corrupted Images', IEEE Trans. Circuits Systems-Il, vol. 46, no. 1, pp.78-80, 1999 https://doi.org/10.1109/82.749102
  6. S. Zhang and M. A. Karim, 'A new impulse detector for switching median filters', IEEE Signal Process. Lett., vol. 9, no. 4,pp. 360-363, Nov. 2002 https://doi.org/10.1109/LSP.2002.805310
  7. Y. L. You, W. Y. Xu, A. Tannenbaum, and M. Kaveh, 'Behavioral analysis of anisotropic diffusion in image processing', IEEE Trans. Image Process., vol. 5, no. 11, pp. 1539-1553, Nov. 1996 https://doi.org/10.1109/83.541424
  8. G. Ramponi, 'Detail-preserving filter for noisy images', Electron. Lett., vol. 31, no. 11, pp. 865-866, May 1996 https://doi.org/10.1049/el:19950619
  9. F. Torkamani-Azar and K. E. Tait, 'Image recovery using the anisotropic diffusion equation', IEEE Trans. Med Imag., vol. 11, no. 2, pp. 221-232, Jan. 1992 https://doi.org/10.1109/42.141646
  10. G. Pok, and J. C. Liu, 'Selective removal of impulse noise based on homogeneity level information', IEEE Trans. Image Process., vol. 12, pp. 85-92, Jan. 2003 https://doi.org/10.1109/TIP.2002.804278
  11. H. L. Eng and K. K. Ma, 'Noise adaptive soft-switching median filter', IEEE Trans. Image Process., vol. 10, no. 2, pp. 242-251, Feb. 2001 https://doi.org/10.1109/83.902289
  12. H. Hwang and R. A. Haddad, 'Adaptive median filters: New algorithms and results', IEEE Trans. Image Proces-, voI.4, no.4, pp.499-502, Apr. 1995 https://doi.org/10.1109/83.370679
  13. P. Ng and K. Ma, 'Switching Median Filter with Boundary Discriminative noise detection', IEEE Trans. Image Process., vol. 15, no. 6, pp. 1506-1516, June 2006 https://doi.org/10.1109/TIP.2005.871129
  14. K. J. Overton and T. E. Weymouth, 'A noise reducing preprocessing algorithm', in Proc. IEEE Computer Science Conf. Pattern Recognition and Image Processing, Chicago, IL, pp. 498-507, 1979
  15. A. C. Bovik:, T. S. Huang, and D. C. Munson, 'A generalization of median filtering using linear combinations of order statistics', IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-31, no. 6, pp. 1342-1350, Dec. 1983
  16. G. Pok, J. Liu, and A. S. Nair, 'Selective removal of impulse noise based on homogeneity level information', IEEE Trans. Image Process., vol. 3, no. 1, pp.192-206, Mar. 1994 https://doi.org/10.1109/83.277900