Logarithmic adaptive neighborhood image processing (LANIP): Introduction, connections to human brightness perception, and application issues

Abstract : A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the general adaptive neighborhood image processing (GANIP) approaches, that allow several intensity and spatial properties of the human brightness perception to be mathematically modeled and operationalized, and computationally implemented. The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Webers and Fechners laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property. The LANIP approach is finally exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications.
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https://hal-emse.ccsd.cnrs.fr/emse-00445939
Contributeur : Andrée-Aimée Toucas <>
Soumis le : lundi 11 janvier 2010 - 15:54:33
Dernière modification le : jeudi 11 janvier 2018 - 06:21:23

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Jean-Charles Pinoli, Johan Debayle. Logarithmic adaptive neighborhood image processing (LANIP): Introduction, connections to human brightness perception, and application issues. EURASIP Journal on Advances in Signal Processing, SpringerOpen, 2007, pp.36105. 〈10.1155/2007/36105〉. 〈emse-00445939〉

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