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Conference Papers Year : 2019

General Adaptive Neighborhood Image Processing (GANIP)

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Abstract

The framework entitled General Adaptive Neighborhood Image Processing (GANIP) has been introduced in order to propose an original local image representation and mathematical structure for adaptive non-linear processing and analysis of gray-tone images. In this talk, the GANIP framework is first presented and particularly studied in the context of image filtering. 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. Several adaptive image transforms are then defined in the context of convolution analysis, order filtering or mathematical morphology. Such operators are no longer spatially invariant, but vary over the whole image with General Adaptive Neighborhoods (GANs) as adaptive operational windows, taking intrinsically into account the local image features. The GANIP framework allows efficient adaptive image filters to be built and opens new pathways that promise large prospects for non-linear image filtering.
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Dates and versions

hal-02067972 , version 1 (14-03-2019)

Identifiers

  • HAL Id : hal-02067972 , version 1

Cite

Johan Debayle. General Adaptive Neighborhood Image Processing (GANIP). 2019 IS&T International Symposium on Electronic Imaging (IE2019) - Image Processing: Algorithms and Systems XVII, Society for Imaging Sciences and Technology, Jan 2019, Burlingame, United States. ⟨hal-02067972⟩
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