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General Adaptive Neighborhood Choquet Image Filtering

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Abstract

A novel framework entitled General Adaptive Neighborhood Image Processing (GANIP) has been recently introduced in order to propose an original image representation and mathematical structure for adaptive image processing and analysis. The central idea is based on the key notion of adaptivity which is simultaneously associated with the analyzing scales, the spatial structures and the intensity values of the image to be addressed. In this paper, the GANIP framework is briefly exposed and particularly studied in the context of Choquet filtering (using fuzzy measures), which generalizes a large class of image filters. The resulting spatially-adaptive operators are studied with respect to the general GANIP framework and illustrated in both the biomedical and materials application areas. In addition, the proposed GAN-based filters are practically applied and compared to several other denoising methods through experiments on image restoration, showing a high performance of the GAN-based Choquet filters.

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  1. Centre CIS-LPMG, UMR CNRS 5148, Ecole Nationale Supérieure des Mines de Saint-Etienne, 158, cours Fauriel, 42023, Saint-Etienne cedex 2, France

    Johan Debayle & Jean-Charles Pinoli

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  1. Johan Debayle
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  2. Jean-Charles Pinoli
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Correspondence to Johan Debayle.

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Debayle, J., Pinoli, JC. General Adaptive Neighborhood Choquet Image Filtering. J Math Imaging Vis 35, 173–185 (2009). https://doi.org/10.1007/s10851-009-0163-0

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