Multivariate statistical modeling for image denoising using wavelet transforms

Signal Processing: Image Communication

Volumn 20, Issue 1, 2005, Pages 77-89

  Cho, Dongwook ^a   Bui, Tien D ^a  

^a Dept of Electrical and Computer Engineering   (Canada)

Author keywords

Estimation; Image denoising; Incorporating wavelet coefficients; Multivariate statistical model; Wavelet transform

Indexed keywords

ESTIMATION; FREQUENCIES; MATHEMATICAL MODELS; SET THEORY; SPURIOUS SIGNAL NOISE; STATISTICAL METHODS; WAVELET TRANSFORMS;

IMAGE DENOISING; INCORPORATING WAVELET COEFFICIENTS; MULTIVARIATE STATISTICAL MODELS; THRESHOLDING;

IMAGE PROCESSING;

EID: 10644256458     PISSN: 09235965     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.image.2004.10.003     Document Type: Article

References (19)

1. T.T. Cai, B.W. Silverman, Incorporating information on neighbouring coefficients into wavelet estimation, Sankhya: Indian J. Statist. 63 (Series B, Pt. 2) (2001) 127-148.
2. S.G. Chang, B. Yu, M. Vetterli, Adaptive wavelet thresholding for image denoising and compression, IEEE Trans. Image Process. 9 (September 2000) 1532-1546.
3. S.G. Chang, B. Yu, M. Vetterli, Spatially adaptive wavelet thresholding with context modeling for image denoising, IEEE Trans. Image Process. 9 (9) (September 2000) 1531-1552.
4. G.Y. Chen, T.D. Bui, Multiwavelet denoising using neighbouring coefficients, IEEE Signal Processing Lett. 10 (7) (2003) 211-214.
5. H. Chipman, E. Kolaczyk, R. McCulloch, Adaptive Bayesian wavelet shrinkage, J. Amer. Statist. Assoc. 92 (440) (1997) 1413-1421.
6. H. Choi, J.K. Romberg, R.G. Baraniuk, N.G. Kingsbury, Hidden Markov tree modeling of complex wavelet transforms, Proceedings of ICASSP, vol. 1, Istanbul, Turkey, June 2000, pp. 133-136.
7. M.S. Crouse, R.D. Nowak, R.G. Baraniuk, Wavelet-based signal processing using hidden markov models, IEEE Trans. Signal Process. 46 (4) (1998) 886-902.
8. D.L. Donoho, Denoising by soft-thresholding, IEEE Trans. Inform. Theory 41 (1995) 613-627.
9. D.L. Donoho, I.M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage, J. Amer. Statist. Assoc. 90 (432) (1995) 1200-1224.
10. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, second ed., Wiley, New York, 2000.
11. C. Fernández, J. Osiewalski, M. Steel, Modelling and inference with v-spherical distributions, J. Amer. Statist. Assoc. 90 (432) (1995) 1331-1340.
12. N.G. Kingsbury, Image processing with complex wavelets, Phil. Trans. Royal Soc. London A 357 (1999) 2543-2560.
13. X. Li, M.T. Orchard, Spatially adaptive image denoising under overcomplete expansion, IEEE International Conference on Image Processing, September 2000.
14. M.K. Mihcak, I. Kozintsev, K. Ramchandran, P. Moulin, Low-complexity image denoising based on statistical modeling of Wavelet Coefficients, IEEE Signal Process. Lett. 6 (12) (1999) 300-303.
15. S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal. Machine Intell. 11 (7) (July 1989) 674-693.
16. J. Portilla, V. Strela, M. Wainwright, E.P. Simoncelli, Image denoising using scale mixtures of Gaussians in the wavelet domain, IEEE Trans. Image Process. 12 (11) (November 2003) 1338-1351.
17. S.J. Roberts, D. Husmeier, I. Rezek, W. Penny, Bayesian approaches to Gaussian mixture modeling, IEEE Trans. Pattern Anal. Machine Intell. 20 (11) (November 1998) 1133-1142.
18. L. Sendur, I.W. Selesnick, Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency, IEEE Trans. Signal Process. 50 (11) (2002) 2744-2756.
19. L. Sendur, I.W. Selesnick, Bivariate shrinkage with local variance estimation, IEEE Signal Process. Lett. 9 (12) (2002) 438-441.