Publication
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.
Touradj Ebrahimi, Michela Testolina, Tomás Soares De Carvalho Feith