The Logarithmic Image Processing Model: Connections with Human Brightness Perception and Contrast Estimators

  • Authors:

  • Jean-Charles Pinoli

  • Affiliations:

  • Pechiney, Centre de Recherches, Parc Economique Centr‘/Alp, BP 27, F38340 Voreppe, France/ and Laboratoire Image, Signal et Acoustique, CNRS EP 92, Ecole Supé/rieure de Chimie, Physique ...

  • Venue:
  • Journal of Mathematical Imaging and Vision
  • Year:
  • 1997

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Abstract

The logarithmic image processing (LIP) model is amathematical framework based on abstract linear mathematicswhich provides a set of specific algebraic and functionaloperations that can be applied to the processing of intensityimages valued in a bounded range. The LIP model has been provedto be physically justified in the setting of transmitted lightand to be consistent with several laws and characteristics ofthe human visual system. Successful application examples havealso been reported in several image processing areas, e.g.,image enhancement, image restoration, three-dimensional imagereconstruction, edge detection and image segmentation.The aim of this article is to show that the LIP model is atractable mathematical framework for image processing which isconsistent with several laws and characteristics of humanbrightness perception. This is a survey article in the sensethat it presents (almost) previously published results in arevised, refined and self-contained form. First, an introductionto the LIP model is exposed. Emphasis will be especially placedon the initial motivation and goal, and on the scope of themodel. Then, an introductory summary of mathematicalfundamentals of the LIP model is detailed. Next, the articleaims at surveying the connections of the LIP model with severallaws and characteristics of human brightness perception, namelythe brightness scale inversion, saturation characteristic, Weber‘sand Fechner‘s laws, and the psychophysical contrast notion. Finally,it is shown that the LIP model is a powerful and tractable framework for handling the contrast notion. This is done througha survey of several LIP-model-based contrast estimators associated with special subparts (point, pair of points,boundary, region) of intensity images, that are justified bothfrom a physical and mathematical point of view.