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Communication dans un congrès
Comparison of orientated and spatially variant morphological filters vs mean/median filters for adaptive image denoising
Abstract : This paper shows a comparison of spatially-variant discrete operators for denoising gray-level images. These non-iterative operators use a neighborhood that varies over space, adapting their shape and orientation according to the data of the image under study. The orientation of the neighborhood is computed by means of a diffusion process of the average square gradient field, which regularizes and extends the orientation information from the edges of the objects to the homogeneous areas of the image; and the shape of the orientated neighborhood can be either a linear segment or a rectangle of anisotropy given by the distance to relevant edges of the objects. Results on gray-level images show the ability of spatially-variant morphological operators for adaptively preserving the main structures in the image while reducing the noise.
Keywords :
Adaptive images
De-noising
Diffusion process
Directional filtering
Discrete operators
Gradient fields
Gray level image
Linear segments
Main structure
Morphological filters
Morphological operator
Non-iterative
Orientation information
Spatially variant filtering
Imaging systems
Mathematical morphology
Mathematical operators
Noise pollution control
Image segmentation
https://hal-mines-paristech.archives-ouvertes.fr/hal-00834435
Contributeur : Bibliothèque Mines Paristech <>
Soumis le : samedi 15 juin 2013 - 08:57:09
Dernière modification le : mercredi 14 octobre 2020 - 03:52:39
Contributeur : Bibliothèque Mines Paristech <>
Soumis le : samedi 15 juin 2013 - 08:57:09
Dernière modification le : mercredi 14 octobre 2020 - 03:52:39