Comparison of orientated and spatially variant morphological filters vs mean/median filters for adaptive image denoising
Résumé
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.
Mots clés
- Morphological filters
- Discrete operators
- Morphological operator
- Non-iterative
- Orientation information
- Spatially variant filtering
- Imaging systems
- Mathematical morphology
- Mathematical operators
- Noise pollution control
- Image segmentation
- Main structure
- Linear segments
- Gray level image
- Gradient fields
- Directional filtering
- Diffusion process
- De-noising
- Adaptive images
