Abstract
This paper focuses on a novel approach for image denoising: WISDOW (Wavelet based Image and Signal De-noising via Overlapping Waves). It is based on approximating any singularity by means of a basic one in a wavelet domain. This approach allows us to reach some interesting mathematical properties along with good performances in terms of both subjective and objective quality. In fact, achieved results are comparable to the best wavelet approaches requiring a low computational effort and resulting completely automatic.
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References
Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, London (1998)
Choi, H., Baraniuk, R.: Analysis of wavelet - domain wiener filters. In: Proceedings of SPIE, San Diego (1997)
Kazubek, M.: Wavelet domain image denoising by thresholding and wiener filtering. IEEE Signal Processing Letters 10, 324–326 (2003)
Portilla, J., Strela, V., Wainwright, M., Simoncelli, E.: Image denoising using scale mixtures of gaussians in the wavelet domain. IEEE Transactions on Image Processing 12, 1338–1351 (2003)
Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation via wavelet shrinkage. Biometrika 81, 425–455 (1994)
Donoho, D.L.: De-noising by soft thresholding. IEEE Transactions on Information Theory 41, 613–627 (1995)
Chang, S., Yu, B., Vetterli, M.: Spatially adaptive thresholding with context modeling for image denoising. IEEE Transactions on Image Processing 9, 1522–1531 (2000)
Mihcak, M., Kozintsev, I., Ramchandran, K., Moulin, P.: Spatially adaptive thresholding with context modeling for image denoising. IEEE Signal Processing Letters 6, 300–303 (1999)
Do, M.N., Vetterli, M.: Contourlets: a new directional multiresolution image representation. In: Proceedings of 36th Asilomar Conference on Signals Systems and Computers 1, 497–501 (2002)
Pennec, E.L., Mallat, S.: Non linear image approximation with bandelets. Tech. Rep. CMAP/ Ecole Polytechnique (2003)
Starck, J.L., Candes, E.J., Donoho, D.L.: The curvelet transform for image denoising. IEEE Transactions on Image Processing 11, 670–684 (2002)
Bruni, V., Vitulano, D.: A wiener filter improvement combining wavelet domains. In: Proceedings of 12th International Conference on Image Analysis and Processing, pp. 518–523 (2003)
Ishwar, P., Ratakonda, K., Moulin, P., Ahuja, N.: Image denoising using multiple compaction domains. Proceedings of ICASSP 1998 3, 1889–1892 (1998)
Bruni, V., Vitulano, D.: Image and signal denoising in a fixed wavelet basis. IAC Report, CNR (2004)
Ishwar, P., Moulin, P.: Multiple domain image modeling and restoration. In: Proceedings of IEEE International Conference on Image Processing, pp. 362–366 (1999)
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Bruni, V., Vitulano, D. (2004). Image De-noising via Overlapping Wavelet Atoms. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30125-7_23
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DOI: https://doi.org/10.1007/978-3-540-30125-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23223-0
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