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 |
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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 | |
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 |
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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