Context-based bias removal of statistical models of wavelet coefficients for image denoising

Weisheng Dong; Xiaolin Wu; Guangming Shi; Lei Zhang

doi:10.1109/ICIP.2009.5414255

Context-based bias removal of statistical models of wavelet coefficients for image denoising

Weisheng Dong, Xiaolin Wu, Guangming Shi, Lei Zhang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

Existing wavelet-based image denoising techniques all assume a probability model of wavelet coefficients that has zero mean, such as zero-mean Laplacian, Gaussian, or generalized Gaussian distributions. While such a zero-mean probability model fits a wavelet subband well, in areas of edges and textures the distribution of wavelet coefficients exhibits a significant bias. We propose a context modeling technique to estimate the expectation of each wavelet coefficient conditioned on the local signal structure. The estimated expectation is then used to shift the probability model of wavelet coefficient back to zero. This bias removal technique can significantly improve the performance of existing wavelet-based image denoisers.

Original language	English
Title of host publication	2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
Publisher	IEEE Computer Society
Pages	3841-3844
Number of pages	4
ISBN (Print)	9781424456543
DOIs	https://doi.org/10.1109/ICIP.2009.5414255
Publication status	Published - 1 Jan 2009
Event	2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt Duration: 7 Nov 2009 → 10 Nov 2009

Conference

Conference	2009 IEEE International Conference on Image Processing, ICIP 2009
Country	Egypt
City	Cairo
Period	7/11/09 → 10/11/09

Keywords

Bayesian shrinkage
Context modeling
Estimation bias
Image denoising

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

More information

10.1109/ICIP.2009.5414255

Cite this

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title = "Context-based bias removal of statistical models of wavelet coefficients for image denoising",

abstract = "Existing wavelet-based image denoising techniques all assume a probability model of wavelet coefficients that has zero mean, such as zero-mean Laplacian, Gaussian, or generalized Gaussian distributions. While such a zero-mean probability model fits a wavelet subband well, in areas of edges and textures the distribution of wavelet coefficients exhibits a significant bias. We propose a context modeling technique to estimate the expectation of each wavelet coefficient conditioned on the local signal structure. The estimated expectation is then used to shift the probability model of wavelet coefficient back to zero. This bias removal technique can significantly improve the performance of existing wavelet-based image denoisers.",

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Dong, W, Wu, X, Shi, G & Zhang, L 2009, Context-based bias removal of statistical models of wavelet coefficients for image denoising. in 2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings., 5414255, IEEE Computer Society, pp. 3841-3844, 2009 IEEE International Conference on Image Processing, ICIP 2009, Cairo, Egypt, 7/11/09. https://doi.org/10.1109/ICIP.2009.5414255

Context-based bias removal of statistical models of wavelet coefficients for image denoising. / Dong, Weisheng; Wu, Xiaolin; Shi, Guangming; Zhang, Lei.

2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings. IEEE Computer Society, 2009. p. 3841-3844 5414255.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - Existing wavelet-based image denoising techniques all assume a probability model of wavelet coefficients that has zero mean, such as zero-mean Laplacian, Gaussian, or generalized Gaussian distributions. While such a zero-mean probability model fits a wavelet subband well, in areas of edges and textures the distribution of wavelet coefficients exhibits a significant bias. We propose a context modeling technique to estimate the expectation of each wavelet coefficient conditioned on the local signal structure. The estimated expectation is then used to shift the probability model of wavelet coefficient back to zero. This bias removal technique can significantly improve the performance of existing wavelet-based image denoisers.

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