Benefits Of Consistency In Image Denoising With Steerable Wavelets

The steerable wavelet transform is a redundant image representation with the remarkable property that its basis functions can be adaptively rotated to a desired orientation. This makes the transform well-suited to the design of wavelet-based algorithms applicable to images with a high amount of directional features. However, arbitrary modification of the wavelet-domain coefficients may violate consistency constraints because a legitimate representation must be redundant. In this paper, by honoring the redundancy of the coefficients, we demonstrate that it is possible to improve the performance of regularized least-squares problems in the steerable wavelet domain. We illustrate that our consistent method significantly improves upon the performance of conventional denoising with steerable wavelets.


Published in:
2013 Ieee International Conference On Acoustics, Speech And Signal Processing (Icassp), 1355-1358
Presented at:
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, CANADA, MAY 26-31, 2013
Year:
2013
Publisher:
New York, Ieee
ISSN:
1520-6149
ISBN:
978-1-4799-0356-6
Keywords:
Laboratories:




 Record created 2014-06-02, last modified 2018-03-17

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