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 proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


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 languageEnglish
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PublisherIEEE Computer Society
Number of pages4
ISBN (Print)9781424456543
Publication statusPublished - 1 Jan 2009
Event2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt
Duration: 7 Nov 200910 Nov 2009


Conference2009 IEEE International Conference on Image Processing, ICIP 2009


  • Bayesian shrinkage
  • Context modeling
  • Estimation bias
  • Image denoising

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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