Empirical multiresolution models applicable to gray-level image processing

摘要

This paper deals with empirical multiresolution linear models intended for image processing. Such models contain information about gray-level composition of regions in the image. First, a general method for building these models from samples of selected images is described. Then, a measure of their quality, based on the Jensen-Shannon divergence, is introduced. This divergence is also used as a distance measure for classifying images. Applications in non-linear image filtering are provided, giving better result than classical median filtering.