Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, an
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Research Article Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues J.-C. Pinoli and J. Debayle Ecole Nationale Sup´erieure des Mines de Saint-Etienne, Centre Ing´enierie et Sant´e (CIS), Laboratoire LPMG, UMR CNRS 5148, 158 cours Fauriel, 42023 Saint-Etienne Cedex 2, France Received 29 November 2005; Revised 23 August 2006; Accepted 26 August 2006 Recommended by Javier Portilla 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. Copyright © 2007 J.-C. Pinoli and J. Debayle. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
In its broad acceptation [1], the notion of processing an image involves the transformation of that image from one form into another. The result may be a new image or may take the form of an abstraction, parametrization, or a decision. Thus, image processing is a large and interdisciplinary field which deals with images. Within the scope of the present article, the term image will refer to a continuous or discrete (including the digital form) two-dimensional distribution of light intensity [2, 3], considered either in its physical or in its psychophysical form. 1.1. Fundamental requirements for an image processing framework In developing image processing techniques, Stockham [1] has noted that it is of central importance that an image processing framework must be physically consistent with the nature of the images, and that the mathematical rules and structures must be compatible with the information to be
processed. Jain [4] has clearly shown the interest and power of mathematics for image representation and processing. Granrath [5] has recognized the important role of human visual laws and models in image processing. He also highlighted the symbiotic relationship between the study of image processing and of the
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