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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.
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Submitted on : Saturday, June 15, 2013 - 8:57:09 AM
Last modification on : Wednesday, October 14, 2020 - 3:52:39 AM

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Rafael Verdu-Monedero, Jesus Angulo, Jorge Larrey-Ruiz, Juan Morales-Sanchez. Comparison of orientated and spatially variant morphological filters vs mean/median filters for adaptive image denoising. 17th IEEE International Conference on Image Processing (ICIP), Sep 2010, Hong-Kong, Hong Kong SAR China. pp.113-116, ⟨10.1109/ICIP.2010.5651909⟩. ⟨hal-00834435⟩

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