융합신호처리학회논문지 (Journal of the Institute of Convergence Signal Processing)

한국융합신호처리학회 (The Korea Institute of Convergence Signal Processing)
권호 표지

영상 잡음제거를 위한 개선된 BAMS 필터

The Improved BAMS Filter for Image Denoising

  • 투고 : 2010.08.18
  • 심사 : 2010.10.29
  • 발행 : 2010.10.30
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초록

BAMS(Baysian Adaptive Multiresolution Smoother) 필터는 모의실험 없이 Bayes 추정에 기초한 웨이블릿 축소기법에 의해 잡음을 제거하며 따라서 실시간 처리가 가능하다. BAMS 필터에 의한 영상잡음 제거 성능은 웨이블릿 분해 각 대역의 잡음분산에 크게 의존한다. 기존의 BAMS 필터는 웨이블릿 분해의 고주파 대역에서 사분위 통계량을 이용하여 잡음분산을 추정하여 잡음을 제거하였다. 본 논문에서는 영상신호의 중간대역을 포함한 잡음제거를 위해 변형된 사분위 통계량 및 모노토닉 변환으로 중간대역 잡음편차 추정하고 이를 이용해서 중간대역 및 고주파 대역의 영상잡음을 제거한 결과 중간대역의 잡음을 제거하므로 약 2[dB]정도의 PSNR이 증가하였으며 잡음편차가 작은 영상의 잡음제거에서도 효과가 있었다.

The BAMS filter is a kind of wavelet shrinkage filter based on the Bayes estimators with no simulation, therefore it can be used for a real time filter. The denoising efficiency of BAMS filter is seriously affected by the estimated noise variance in each wavelet band. To remove noise in signals in existing BAMS filter, the noise variance is estimated by using the quartile of the finest level of details in the wavelet decomposition, and with this variance, the noise of the level is removed. In this paper, to remove the image noise includingodified quartile of the level of detail is proposed. And by these techniques, the image noises of mid and high frequency bands are removed, and the results showed that the increased PSNR of ab the midband noise, the noise variance estimation method using the monotonic transform and the mout 2[dB] and the effectiveness in denosing of low noise deviation images.

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참고문헌

  1. D. L. Donoho, I. M. Johnstone, "Ideal Spatial Adaptation by Wavelet Shrinkage", Biometrika, 81. 3. pp425-455, 1994. https://doi.org/10.1093/biomet/81.3.425
  2. G. Nason, " Wavelet Regression by cross-Validation" , J. Roy. Statist. Soc., Ser., B, 58, pp.463-479,1996.
  3. H. Y. Gao, " Wavelet Shrinkage Denoising Using the Non-Negative Garrote", Journal of Computational and Graphical Statistics, 7(4), Dec., pp. 469-488, 1998. https://doi.org/10.2307/1390677
  4. N. Saito, " Simultaneous Noise Suppression and Signal Compression Using a Library of Orthonormal bases and the Minimax Description Length Criterion", In Wavelets in Geophysics, Foufoua Geotgion and Kumar(eds), Academic Press, 1994.
  5. G. Nason, " Wavelet Funtion Estimation Using Cross-Validation", In Wavelets and Statistics, A. Antoniadis and G. Oppenheim(eds), Springer-Varlag. pp. 261-280, 1995.
  6. S. Mallat, A Wavelet Tour of Signal Processing, 2nd., Academic Press, pp. 434-524, 1999.
  7. Stan Z. Li, Markov Random Field Modeling in Image Analysis, Springer, 2001.
  8. B. Vidakovic, " Nonlinear Wavelet Shrinkage With Bayes Rules and Bayes Factors", J. Amer. Statist. Assoc. 93, pp. 173-179, 1998. https://doi.org/10.2307/2669614
  9. F. Abramovich, T. Sapatinas and B. W. Silverman, "Wavelet Thresholding via a Bayesian Approach", J. Ray. Statist. soc. B, 60, pp. 725-749, 1998.
  10. H. A. Chipman, I. J. Wolfson, " Prior Elicitation in the Wavelet Domain", In Bayesian Inference in Wavelet -Based Models, P. MUller and B. Vidakovic(eds), New-York: Springer, pp. 83-94, 1998.
  11. ER-HU ZHANG, SHU-TING HUANG, "A New Image Denoising Method Based on the Dependency Wavelet Coefficients" Preceeding of the Third International Conference on Machine Learning and Cybernetics, Shanghai, August, pp. 3841-3844, 2004.
  12. Levent Sendure, Ivan W. Selesnick, "Bivariate Shrinkage with Local variance Estimation ", IEEE Signal Processing Letters, vol. 9, No, 12 Dec. 2002.
  13. B. Vidakovic, F. Rugger, " BAMS Method: Theory and Simulations", the Indian Journal of Statics Invited Paper for Special Issue on Wavelet Methods, vol. 63, ser. B, 2001.
  14. 우창용, 박남천, "웨이브릿 기저의 영상신호에서 단조변환으로 추정된 잡음편차를 사용한 VisuShrink 기법의 잡음제거", 신호처리.시스템학회지, 제5권, 2호, 2004년, 4월, pp111-118.
  15. T.Blu and F.Luisier"The SURE-LET Approach to Image Denoising" IEEE Transaction on Image Processing Vol16 No11 November 2007.