Adaptive noise-reducing anisotropic diffusion filter. Issue 5 (July 2016)

• Record Type: Journal Article
• Authors: Ben Abdallah, Mariem; Malek, Jihene; Azar, Ahmad; Belmabrouk, Hafedh; Esclarín Monreal, Julio; Krissian, Karl
• Is Part Of: Neural computing & applications. Volume 27:Issue 5(2016)
• Page Start: 1273
• Page End: 1300
• Publication Date: 2016-07
• Subjects: CCD cameras -- Anisotropic diffusion -- Filtering -- Noise estimation; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1933-9
• Languages: English
• ISSNs: 0941-0643

Abstract

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.