Patch redundancy in images: a statistical testing framework and some applications [article]

De Bortoli Valentin, Desolneux Agnès, Galerne Bruno, Leclaire Arthur
2019 arXiv   pre-print
In this work we introduce a statistical framework in order to analyze the spatial redundancy in natural images. This notion of spatial redundancy must be defined locally and thus we give some examples of functions (auto-similarity and template similarity) which, given one or two images, computes a similarity measurement between patches. Two patches are said to be similar if the similarity measurement is small enough. To derive a criterion for taking a decision on the similarity between two
more » ... es we present an a contrario model. Namely, two patches are said to be similar if the associated similarity measurement is unlikely to happen in a background model. Choosing Gaussian random fields as background models we derive non-asymptotic expressions for the probability distribution function of similarity measurements. We introduce a fast algorithm in order to assess redundancy in natural images and present applications in denoising, periodicity analysis and texture ranking.
arXiv:1904.06428v1 fatcat:35jn4vkl4jdltjhpo4n2dpsho4