nach oben

Neural Computing and Applications

Erschienen in:

05.05.2016 | Original Article

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

Erschienen in: Neural Computing and Applications | Sonderheft 1/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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).

Vorheriger Artikel Estimation of soil dispersivity using soft computing approaches

Nächster Artikel Collaborative representation analysis methods for feature extraction

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Tseng C-C, Lee S-L (2014) Digital image sharpening using Riesz fractional order derivative and discrete Hartley transform. In: 2014 IEEE Asia Pacific conference on circuits and systems (APCCAS). IEEE

Ibrahim RW, Jalab HA (2013) Time-space fractional heat equation in the unit disk. In: Trujillo JJ (ed) Abstract and applied analysis. Hindawi Publishing Corporation, New York, USA

Jalab HA, Ibrahim RW (2015) Fractional Alexander polynomials for image denoising. Sig Process 107:340–354CrossRef

Jalab HA, Ibrahim RW (2014) Fractional conway polynomials for image denoising with regularized fractional power parameters. J Math Imaging Vis 51(3):1–9

Jalab H, Ibrahim R (2016) Image denoising algorithms based on fractional sinc α with the covariance of fractional Gaussian fields. Imaging Sci J 64:100–108

Yu Q et al (2013) The use of a Riesz fractional differential-based approach for texture enhancement in image processing. ANZIAM J 54:590–607MathSciNetCrossRef

Podlubny I (1999) Fractional differential equations. Acadamic Press, London, p E2

Miller KS, Ross B (1993) An introduction to the fractional calculus and fractional differential equations. Wiley, New YorkMATH

Kilbas AAA, Srivastava HM, Trujillo JJ (2006) Theory and applications of fractional differential equations, vol 204. Elsevier, AmsterdamCrossRefMATH

10.

Hilfer R et al (2000) Applications of fractional calculus in physics, vol 128. World Scientific, SingaporeCrossRefMATH

11.

Ortigueira MD (2006) Riesz potential operators and inverses via fractional centred derivatives. Int J Math Math Sci 2006:1–12

12.

Mathai AM, Haubold HJ (2013) On a generalized entropy measure leading to the pathway model with a preliminary application to solar neutrino data. Entropy 15(10):4011–4025MathSciNetCrossRefMATH

13.

Tsallis C (2009) Introduction to nonextensive statistical mechanics. Springer, BerlinMATH

14.

Wang Z et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRef

15.

Gonzales RC, Woods RE, Eddins SL (2004) Digital image processing using MATLAB. Pearson Prentice Hall, Englewood Cliffs

16.

Cuesta E, Kirane M, Malik SA (2012) Image structure preserving denoising using generalized fractional time integrals. Sig Process 92(2):553–563CrossRef

17.

Zhang Y-S et al (2014) Fractional domain varying-order differential denoising method. Opt Eng 53(10):102102-1–102102-7

18.

Hu J, Pu Y, Zhou J (2011) A novel image denoising algorithm based on riemann-liouville definition. J Comput 6(7):1332–1338

19.

Jalab HA, Ibrahim RW (2012) Denoising algorithm based on generalized fractional integral operator with two parameters. Discrete Dyn Nat Soc 2012:1–14

Titel: Image denoising algorithm based on the convolution of fractional Tsallis entropy with the Riesz fractional derivative
Publikationsdatum: 05.05.2016
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2331-7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Sonderheft 1/2017

Ellipsoidal support vector data description

Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance

A modified augmented Lagrangian with improved grey wolf optimization to constrained optimization problems

Finite-time stabilization of uncertain neural networks with distributed time-varying delays

Reducing the complexity of an adaptive radial basis function network with a histogram algorithm

Classifying biomedical knowledge in PubMed using multi-label vector machines with weaker optimization constraints