Image Denoising Based on Adapted Dictionary Computation
This paper introduces a new denoising technique that consists in recovering the image using a filtering function adapted to the image content. The definition of such a function relies on the computation of similarity between pixels of a given neighborhood. Our contribution consists in the definition of a new similarity criterion which is more robust to noise. This measure is computed from a dictionary that is adapted to image content. The projection of the image content to this subspace are used then to define a metric between a pixel and the neighborhood ones. Very promising experimental results show the potential of our approach.