Scale Selection for Compact Scale-Space Representation of Vector-Valued Images

This paper investigates the scale selection problem for vector-valued nonlinear diffusion scale-spaces. We present a new approach for the localization scale selection, which aims at maximizing the image content's presence by finding the scale having a maximum correlation with the noise-free image. For scale-space discretization, we propose to address an adaptation of the optimal diffusion stopping time criterion introduced by Mrazek and Navara [1], in such a way that it identifies multiple scales of importance.

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