Image denoising algorithm based on the convolution of fractional Tsallis entropy with the Riesz fractional derivative

Jalab, Hamid A. and Ibrahim, Rabha W. and Ahmed, Amr (2017) Image denoising algorithm based on the convolution of fractional Tsallis entropy with the Riesz fractional derivative. Neural Computing and Applications, 28 (sup. 1). pp. 217-223. ISSN 0941-0643

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Abstract

Image denoising is an important component of image processing. The interest in the use of Riesz fractional order derivative has been rapidly growing for image processing recently. This paper mainly introduces the concept of fractional calculus and proposes a new mathematical model in using the convolution of fractional Tsallis entropy with the Riesz fractional derivative for image denoising. The structures of n x n fractional mask windows in the x and y directions of this algorithm are constructed. The image denoising performance is assessed using the visual perception, and the objective image quality metrics, such as peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The proposed algorithm achieved average PSNR of 28.92 dB and SSIM of 0.8041. The experimental results prove that the improvements achieved are compatible with other standard image smoothing filters (Gaussian, Kuan, and Homomorphic Wiener). © 2016 The Natural Computing Applications Forum

Keywords:Algorithms, Calculations, Convolution, Entropy, Image processing, Image quality, Signal to noise ratio, Fractional calculus, Fractional derivatives, Fractional masks, Fractional order derivatives, Image denoising algorithm, Peak signal to noise ratio, Structural similarity indices (SSIM), Tsallis entropies, Image denoising
Subjects:G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Computer Science
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ID Code:23762
Deposited On:19 Aug 2016 10:22

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