Visual Information from Anisotropic Transformations

Loss of information in images undergoing fine-to-coarse image transformations is analyzed by using an approach based on the theory of irreversible transformations. It is shown that entropy variation along scales can be used to characterize basic, low-level information and to gauge essential perceptual components of the image. The method is extended to the case of anisotropic diffusion and an algorithm based on entropy variation is presented that extracts relevant features of the image, showing in particular how to discriminate between smooth, textured and edge-type regions