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Local regularity, wavelet leaders and total variation based procedures for texture segmentation

Texture segmentation constitutes a standard image processing task, crucial to many applications. The originality of the present approach resides in the combination of three key ingredients: First, texture characterization relies on the concept of local regularity ; Second, estimation of local regularity is based on new multiscale quantities referred to as wavelet leaders ; Third, segmentation from local regularity faces a fundamental bias variance trade-o: In nature, local regularity estimation shows high variability that impairs the detection of changes, while a posteriori smoothing of regularity estimates precludes from locating correctly changes. Instead, the present contribution proposes several variational problem formulations based on total variation and proximal resolutions that eectively circumvent this trade-o. Performances of the proposed procedures are quantied and compared on synthetic as well as realworld textures.