A Dyadic Wavelet Filtering Method for 2-D Image Denoising
Yonggui Zhu, Xiaolan Yang
DOI: 10.4236/jsip.2011.24044   PDF    HTML     4,998 Downloads   8,584 Views   Citations


We improve spatially selective noise filtration technique proposed by Xu et al. and wavelet transform scale filtering approach developed by Zheng et al. A novel dyadic wavelet transform filtering method for image denoising is proposed. This denoising approach can reduce noise to a high degree while preserving most of the edge features of images. Different types of images are employed to test in the numerical experiments. The experimental results show that our filtering method can reduce more noise contents while maintaining more edges than hard-threshold, soft-threshold filters, Xu’s method and Zheng’s method.

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Y. Zhu and X. Yang, "A Dyadic Wavelet Filtering Method for 2-D Image Denoising," Journal of Signal and Information Processing, Vol. 2 No. 4, 2011, pp. 308-315. doi: 10.4236/jsip.2011.24044.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] S. Mallat and S. Zhong, “Characterization of Signal from Multiscale Edges,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 7, 1992, pp. 710-732. doi:10.1109/34.142909
[2] S. Mallat and W. L. Hwang, “Singularity Detection and Processing with Wavelets,” IEEE Transactions on Information Theory, Vol. 38, No. 2, 1992, pp. 617-643. doi:10.1109/18.119727
[3] J. L. Starck and A. Bijaoui, Filtering and Deconvolution by the Wavelet Transform,” Signal Processing, Vol. 35, No. 3, 1994, pp. 195-211. doi:10.1016/0165-1684(94)90211-9
[4] A. Bijaoui, “Wavelets, Gaussian and Wiener Filtering,” Signal Processing, Vol. 82, No. 4, 2002, pp. 709-712. doi:10.1016/S0165-1684(02)00137-8
[5] P. L. Shui, “Image Denoising Algorithm via Best Wavelet Packet Base Using Wiener Cost Function,” Institution of Engineering and Technology Image Processing, Vol. 1, No. 3, 2007, pp. 311-318.
[6] Y. Xu, J. B. Weaver, et al., “Wavelet Transform Domain Filters: A Spatially Selective Noise Filtration Technique,” IEEE Transactions on Image Processing, Vol. 3, No. 6, 1994, pp. 747-758.
[7] C. Okechukwu and C. Ugweje, “Selective Noise Filtration of Image Signals Using Wavelet Transform,” Imaging Measurement Systems, Vol. 36, No. 3-4, 2004, pp. 279-287.
[8] M. Nasri and H. Nezamabadi-pour, “Image Denoising in the Wavelet Domain Using a New Adaptive Thresholding Function,” Neurocomputing, Vol. 72, No. 4-6, 2009, pp. 1012-1025. doi:10.1016/j.neucom.2008.04.016
[9] Y. Zheng, D. B. H. Tay, et al., “Signal Extraction and Power Spectrum Estimation Using Wavelet Transform Scale Space Filtering and Bayes Shrinkage,” Signal Processing, Vol. 80, No. 8, 2000, pp. 1535-1549. doi:10.1016/S0165-1684(00)00054-2
[10] Y. Leung, J. S. Zhang and Z. B. Xu, “Clustering by Scale-Space Filtering,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, 2000, pp. 1396-1410.
[11] J. Kalif, S. Mallat and B. Rouge, “Deonvolution by Thresholding in Mirror Wavelet Bases,” IEEE Transaction on Image Processing, Vol. 12, No. 4, 2003, pp. 446-457. doi:10.1109/TIP.2003.810592
[12] Y. F. Zheng and R. L. Ewing, “Feature-Based Wavelet Shrinkage Algorithm for Image Denoising,” IEEE Transaction on Image Processing, Vol. 14, No. 12, 2005, pp. 2024-2039.
[13] Z. Y. Chen, X. P. Guo, X. L. Zhang, W. J. Cram and Z. W. Li, “A Novel Method for Analysis of Single Ion Channel Signal Based on Wavelet Transform,” Computers in Biology and Machine, Vol. 37, No. 4, 2007, pp. 559-562. doi:10.1016/j.compbiomed.2006.08.006
[14] D. L. Donoho and I. M. Johnstone, “Ideal Spatial Adaption via Wavelet Shrinkage,” Biometrika, Vol. 81, No. 3, 1994, pp. 425-435. doi:10.1093/biomet/81.3.425
[15] D. L. Donoho, “De-Noising by Soft-Thresholding,” IEEE Transaction on Information Theory, Vol. 41, No. 3, 1995, pp. 613-627. doi:10.1109/18.382009
[16] M. Holschneider, R. Kronland-Martinet, J. Morlet and P. Tchamitchian, “Wavelets, Time-Frequency Methods and Phase Space, Chapter A Real-Time Algorithm for Signal Analysis with the Help of the Wavelet Transform,” Springer-Verlag, Berlin, 1989, pp. 289-297.
[17] A. Witkin, “Scale Space Filtering,” Proceedings of 8th International Joint Conference on Artificial Intelligence, Karlsruhe, 1983, pp. 1019-1022.
[18] S. Mallat, “A Wavelet Tour of Signal Processing,” 2nd Edition, Academic Press, New York, 1999.

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