Wavelet Denoising Using Region Merging

영역 병합을 이용한 웨이블릿 잡음 제거

  • 엄일규 (밀양대학교 정보통신공학과) ;
  • 김유신 (부산대학교 컴퓨터 및 정보통신 연구소)
  • Published : 2005.03.01

Abstract

In this paper, we propose a novel algorithm for determining the variable size of locally adaptive window using region-merging method. A region including a denoising point is partitioned to disjoint sub-regions. Locally adaptive window for denoising is obtained by selecting Proper sub-lesions. In our method, nearly arbitrarily shaped window is achieved. Experimental results show that our method outperforms other critically sampled wavelet denoising scheme.

Keywords

References

  1. M. K. Mihcak, I. Kozintsev, K. Ramchandran, and P. Moulin, 'Low-complexity image denoising based on statistical modeling of wavelet coefficients,' IEEE Signal Processing Letters, vol.6, pp.300-303, 1999
  2. L. Sendur, and I. W. Selesnick, 'Bivariate shrinkage with local variance estimation,' IEEE Signal Processing Letters, vol.9, no.12, pp.438-441, 2002 https://doi.org/10.1109/LSP.2002.806054
  3. Z, Cai, T. H. Cheng, C. Lu, and K. R. Subramanian, 'Efficient wavelet-based image denoising algorithm,' Electronics Letters, vol.37, no. 11, pp.683-685, 2001 https://doi.org/10.1049/el:20010466
  4. M. K. Mihcak, I. Kozintsev, K. Ramchandran, 'Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising,' Proc. IEEE Int. Conf. Acous., Speech and Signal Processing, vol. 6, pp. 3253-3256, 1999
  5. J. Liu and P. Moulin, 'Image denoising based on scale-space mixture modeling of wavelet coefficients,' Proc. IEEE Int. Conf. on Image Processing, Kobe, Japan, 1999
  6. M. S. Crouse, R. D. Nowak, and R.G. Baraniuk, 'Wavelet-based statistical signal processing using hidden Markov models,' IEEE. Trans. Image Processing, vol.46, pp. 886-902, 1998 https://doi.org/10.1109/78.668544
  7. J. K. Romberg, H. Choi, and R. G. Baraniuk, 'Bayesian tree-structured image modeling using wavelet-domain hidden Markov models,' IEEE. Trans. Image Processing, vol.10, pp. 1056-1068, 2001 https://doi.org/10.1109/83.931100
  8. H. Choi, J. Romberg, R. Baraniuk, and N. Kingsbury, 'Hidden Markov tree modeling of complex wavelet transforms,' Proc. IEEE Int. Conf. Acous., Speech and Signal Processing, Istanbul, Turkey, June, 2000
  9. I. Daubechies, Ten Lectures on Wavelets. Philadelphia, PA: SIAM, 1992
  10. S. G. Chang, B. Yu, and M. Vetteri, 'Spatially adaptive wavelet thresholding with context modeling for image denosing,' IEEE. Trans. Image Processing, vol.9, pp. 1522-1531, 2000 https://doi.org/10.1109/83.862630
  11. J. M.. Park, W. J.Song, and W. A. Pearlman, 'Speckle filtering of SAR images based on adaptive windowing,' IEE Proceedings Vision, Image and Signal Processing, 146(33), pp.191-197, 1999 https://doi.org/10.1049/ip-vis:19990550
  12. Y. Boykov, P. Veksler, and R. Zabih, 'A variable window approach to early vision,' IEEE Transactions on Pattern Analysis and Machine Intelligence' vol.20, no. 12, pp.1283-1294. 1998 https://doi.org/10.1109/34.735802
  13. P. Balan, and P. M. Mather, 'An adaptive filter for removal of noise in interferometrically derived digital elevation models,' IEEE International Symposium on Geoscience and Remote Sensing, vol.6, pp.2529-2531, 2001