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

H.A. Jalab, R.W. Ibrahim, A. Ahmed

Research output: Contribution to journalArticle (journal)peer-review

29 Citations (Scopus)

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 × 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).
Original languageEnglish
Pages (from-to)217-223
JournalNeural Computing and Applications
Volume28
Issue numbersupp 1
DOIs
Publication statusPublished - 5 May 2017

Research Centres

  • Centre for Intelligent Visual Computing Research

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