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Adaptive Selectivity Frame for Image Denoising

M. El Aallaoui, A. El Bouhtouri, A. Ayadi


Two-dimensional wavelet analysis and directional frames are efficient in the analysis and the decomposition of oriented features in images. However, since wavelets share the same angular selectivity, isotropic, directional and less-oriented features are processed under the same framework with the same number of coefficients.
We propose here a solution to solve this issue. We develop an adaptive representation for all image elements, ranging from highly directional ones to fully isotropic ones, by decomposing them into a frame of directional wavelets with variable angular selectivity.
In the particular context of denoising of images plagued by white noise, after usual thresholding of the wavelet coefficients, our adaptive representation compares favorably to wavelet-based, curvelets and fixed selectivity reconstructions.

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