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01.07.2016 | Original Article

Adaptive noise-reducing anisotropic diffusion filter

verfasst von: Mariem Ben Abdallah, Jihene Malek, Ahmad Taher Azar, Hafedh Belmabrouk, Julio Esclarín Monreal, Karl Krissian

Erschienen in: Neural Computing and Applications | Ausgabe 5/2016

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Abstract

In image processing and computer vision, the denoising process is an important step before several processing tasks. This paper presents a new adaptive noise-reducing anisotropic diffusion (ANRAD) method to improve the image quality, which can be considered as a modified version of a speckle-reducing anisotropic diffusion (SRAD) filter. The SRAD works very well for monochrome images with speckle noise. However, in the case of images corrupted with other types of noise, it cannot provide optimal image quality due to the inaccurate noise model. The ANRAD method introduces an automatic RGB noise model estimator in a partial differential equation system similar to the SRAD diffusion, which estimates at each iteration an upper bound of the real noise level function by fitting a lower envelope to the standard deviations of pre-segment image variances. Compared to the conventional SRAD filter, the proposed filter has the advantage of being adapted to the color noise produced by today’s CCD digital camera. The simulation results show that the ANRAD filter can reduce the noise while preserving image edges and fine details very well. Also, it is favorably compared to the fast non-local means filter, showing an improvement in the quality of the restored image. A quantitative comparison measure is given by the parameters like the mean structural similarity index and the peak signal-to-noise ratio.

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Titel: Adaptive noise-reducing anisotropic diffusion filter
verfasst von: Mariem Ben Abdallah
Jihene Malek
Ahmad Taher Azar
Hafedh Belmabrouk
Julio Esclarín Monreal
Karl Krissian
Publikationsdatum: 01.07.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 5/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-1933-9

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