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

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Image Processing for Mixed Noise Removal

복합 잡음 제거를 위한 영상처리

  • 이경효 (부경대학교 전기제어공학부) ;
  • 김남호 (부경대학교 전기제어공학부)
  • Published : 2009.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

There are Impulse noise and AWGN in a general image processing. Various methods have been proposed to remove these noises. Well-known filters are Mean, Min-max and Median filter and these show different characteristics depending on the noises. When Impulse noise and AWGN are in superposition environment, single filter doesn't remove noises well. Therefore in this paper, we suggested a switching filter using a probability of noise to restore images in this environment. And we compared a filter with conventional method through simulations.

잡음은 임펄스 잡음과 AWGN(additive white gaussian noise)이 있으며, 이들 잡음을 제거하기 위한 다양한 방법들이 제안되었다. 잘 알려진 필터로는 Mean, Min-max, Median 필터 등이 있으며, 이러한 필터들은 잡음에 따라 각기 다른 특성을 나타낸다. 그리고 임펄스 잡음과 AWGN이 중첩된 환경에서는 단일 필터로서는 잡음제거에 있어 한계가 있다. 따라서 본 논문에서는 이러한 환경에서 영상을 복원하기 위해, 잡음확률을 이용한 필터를 제안하였으며,시뮬레이션을통해기존의방법들과그성능을비교하였다.

Keywords

References

  1. Hough, P.V.C. 'Method and Means for Recognizing Complex Patterns.' U.S. Patent. 3,069,654. 1962
  2. A. Restrepo and A. C. Bovik, 'adaptive trimmed mean filters for image restoration', IEEE Trans. Signal Process., vol. 36, pp. 1326-1337, Aug. 1988 https://doi.org/10.1109/29.1660
  3. M. Werman and S. Peleg, 'Min-max operators in texture analysis', IEEE Trans. PAMI, vol. 7, pp. 730-733, Nov. 1985 https://doi.org/10.1109/TPAMI.1985.4767732
  4. Y.Xu and E. M. Lai, 'Restoration of images contaminated by mixed Gaussian and impulse noise using a recursive minimum-maximum method', IEE Proc., Vis. Image Signal Process., vol. 145, pp. 264-270, 1998 https://doi.org/10.1049/ip-vis:19981995
  5. P. S. Windyga, 'Fast impulsive noise removal', IEEE Trans. Image Processing, vol. 10, pp. 173-179, Jan. 2001 https://doi.org/10.1109/83.892455
  6. Xin Wang, 'Generalized Multistage Median Filter', IEEE Trans. Image Processing, vol. 1, pp. 1834-1838, 1992
  7. L. Yin, R. Yang,M. Gabbouj and Y. Neuvo, 'Weighted median filters: a tutorial', IEEE Trans. Circuits Syst., vol. 43, pp. 157-192, 1996 https://doi.org/10.1109/82.486465
  8. S. J. Ko and Y. H. Lee, 'Center weighted median filters and their application to image enhan -cement', IEEE Trans. Circuits Syst., vol. 38, pp. 984-993, Sept. 1991 https://doi.org/10.1109/31.83870
  9. H. Hwang and R. A. Haddad, 'Adaptive median filters: New algorithms and results', IEEE Trans. Image Process., vol. 4, no. 4, pp. 499-502, Apr. 1995 https://doi.org/10.1109/83.370679
  10. 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
  11. T. Chen and Hong Ren Wu, 'Adaptive Impulse Detection Using Center-Weighted Median Filters', IEEE Trans. Signal Processing Lett., vol. 8, pp. 1-3, 2001 https://doi.org/10.1109/97.889633
  12. 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