Empirical multiresolution models applicable to gray-level image processing

M. J. García-Salinas

doi:10.1016/S0923-5965(96)00036-7

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Empirical multiresolution models applicable to gray-level image processing

1997-12-01

PRE

M. J. García-Salinas

Juan Francisco Gómez-Lopera

Pedro L. Luque‐Escamilla

José Martı́nez-Aroza

José C. Segura

DOI：10.1016/S0923-5965(96)00036-7

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Abstract

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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. (C) 1997 Published by Elsevier Science B.V.

Keywords:

gray-level image

multiresolution histogram relationships

probabilistic linear empirical models

model-based filtering

image classification

Jensen-Shannon divergence

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