General Adaptive Neighborhood Image Restoration, Enhancement and Segmentation

  • Johan Debayle
  • Yann Gavet
  • Jean-Charles Pinoli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4141)


This paper aims to outline the General Adaptive Neighborhood Image Processing (GANIP) approach [1–3], which has been recently introduced. An intensity image is represented with a set of local neighborhoods defined for each point of the image to be studied. These so-called General Adaptive Neighborhoods (GANs) are simultaneously adaptive with the spatial structures, the analyzing scales and the physical settings of the image to be addressed and/or the human visual system. After a brief theoretical introductory survey, the GANIP approach will be successfully applied on real application examples in image restoration, enhancement and segmentation.


Image Enhancement Image Restoration Operational Window Mathematical Morphology Gray Tone 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Johan Debayle
    • 1
  • Yann Gavet
    • 1
  • Jean-Charles Pinoli
    • 1
  1. 1.Laboratoire LPMG, UMR CNRS 5148Ecole Nationale Supérieure des Mines de Saint-Etienne, Centre Ingénierie et Santé (CIS)Saint-EtienneFrance

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