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Fractional nonlinear anisotropic diffusion with p-Laplace variation method for image restoration

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Abstract

In this paper, a novel class of fractional-order nonlinear anisotropic diffusion equations based image restoration model is established, which employs the p-Laplace norm of fractional-order gradient of an image intensity function. The role of the fractional-order gradient is to better accommodate the texture details of an image, and the adaptive factor p can be used to diffuse adaptively according to local geometry features, which are fractional-order curvature and fractional-order gradient of an image. Besides removing noise and non-linearly keeping high-frequency edge of images efficiently, our proposed model can enhance the texture details of images and greatly eliminate the staircase effects and also the speckle effects. Fourier transform technique is also proposed to compute the fractional order derivative. Experimental results illustrate that our proposed model can deal with edge preserving and texture enhancing, more efficiently than the other four methods and outperform the other four methods by means of PSNR. Our average PSNR is closed to 1dB higher than the average PSNRs of the other four methods.

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References

  1. Bai J, Feng XC (2007) Fractional-order anisotropic diffusion for image denoising. IEEE Trans Image Process 16(10):2492–2502

    Article  MathSciNet  Google Scholar 

  2. Bergounioux M, Trélat E (2010) A variational method using fractional order Hilbert spaces for tomographic reconstruction of blurred and noised binary images. J Funct Anal 259(9):2296–2332

    Article  MathSciNet  MATH  Google Scholar 

  3. Blomgren PV, Chan TF (2000) Color TV: total variation method for restoration of vector valued images. IEEE Trans Image Process 9(10):1723–1730

    Article  MathSciNet  Google Scholar 

  4. Blomgren PV, Chan TF, Mulet P, Wong C (1997) Total variation image restoration: numerical methods and extensions. In: Proc. IEEE ICIP, 3, pp 384–387

  5. Chan T, Marquina A, Mulet P (2000) High-order total variation-based image restoration. SIAM J Sci Comput 22(2):503–516

    Article  MathSciNet  MATH  Google Scholar 

  6. Chen JJ, Guo J (2010) Image restoration based on adaptive p-Laplace diffusion. In: Proc. ICISP, pp 143–146

  7. Chen YM, Levine S, Rao M (2006) Variable exponent, linear growth functionals in image restoration. SIAM J Appl Math 66(4):1383–1460

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  9. Dobrosotskaya JA, Bertozzi AL (2008) A wavelet-Laplace variational technique for image deconvolution and inpainting. IEEE Trans Image Process 17(5):657–663

    Article  MathSciNet  Google Scholar 

  10. Hua JY, Kuang WK, Gao Z, Meng LM, Xu ZJ (2014) Image denoising using 2-D FIR filters designed with DEPSO. Muiltimed Tools Appl 69(1):157–169

    Article  Google Scholar 

  11. Janev M, Pilipović S, Atanacković T, Obradović R, Ralević N (2011) Fully fractional anisotropic diffusion for image denoising. Math Comput Model 54(1–2):729–741

    Article  MathSciNet  MATH  Google Scholar 

  12. Jung M, Bresson X, Chan TF, Vese LA (2011) Nonlocal Mumford-Shah regularizers for color image restoration. IEEE Trans Image Process 20(6):1583–1598

    Article  MathSciNet  Google Scholar 

  13. Keshari S, Modani SG (2012) Color image encryption scheme based on 4-weighted fractional Fourier transform. J Electron Imaging 21(3):033018

    Article  Google Scholar 

  14. Kristály A, Lisei H, Varga C (2008) Multiple solutions for p-Laplacian type equations. Nonlinear Anal 68(5):1375–1381

    Article  MathSciNet  MATH  Google Scholar 

  15. Li F, Li ZB, Pi L (2010) Variable exponent functionals in image restoration. Appl Math Comput 216(3):870–882

    Article  MathSciNet  MATH  Google Scholar 

  16. Lindqvist P (2006) Notes on p-Laplace equation. University of Jyväskylä -Lectures notes

  17. Liu SB (2001) Existence of solutions to a superlinear p-Laplacian equation. Electron J Differ Equ 66:1–6

    Article  MathSciNet  Google Scholar 

  18. Mathieu B, Melchior P, Oustaloup A, Ceyral C (2003) Fractional differentiation for edge detection. Signal Process 83(11):2421–2432

    Article  MATH  Google Scholar 

  19. Milanfar P (2013) A tour of modern image filtering. IEEE Signal Proc Mag 30(1):106–128

    Article  MathSciNet  Google Scholar 

  20. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639

    Article  Google Scholar 

  21. Podlubny I (1999) Fractional differential equations. Academic, New York

    MATH  Google Scholar 

  22. Pu YF, Wang WX, Zhou JL, Wang YY, Jia HD (2008) Fractional differential approach to detecting textural features of digital image and its fractional differential filter implementation. Sci China Ser F 51(9):1319–1339

    Article  MathSciNet  MATH  Google Scholar 

  23. Ruding L, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Phys D 60(1–4):259–268

    Article  MathSciNet  MATH  Google Scholar 

  24. Strong DM, Chan TF (2003) Edge-preserving and scale-dependent properties of total variation regularization. Inverse Probl 19(6):165–187

    Article  MathSciNet  MATH  Google Scholar 

  25. Tang LL, Huang CT, Pan JS, Liu CY (2013) Dual watermarking algorithm based on the Fractional Fourier Transform. Muiltimed Tools Appl. doi:10.1007/s11042-013-1531-8

    Google Scholar 

  26. Unser M (1999) Fractional Splines, a perfect fit for signal and image processing. IEEE Signal Process Mag 16(6):22–38

    Article  Google Scholar 

  27. Unser M, Blu T (2000) Fractional splines and wavelets. SIAM Rev 42(1):43–67

    Article  MathSciNet  MATH  Google Scholar 

  28. Yahya AA, Tan JQ, Hu M (2013) A blending method based on partial differential equations for image denoising. Muiltimed Tools Appl. doi:10.1007/s11042-013-1586-6

    Google Scholar 

  29. You YL, Xu W, Tannenbaum A, Kaveh M (1998) Behavioral analysis of anisotropic diffusion in image processing. IEEE Trans Image Process 7(3):304–309

    Article  Google Scholar 

  30. Zhang HY, Peng QC, Wu YD (2007) Wavelet inpainting based on p-Laplace operator. Acta Autom Sin 33(5):546–549

    Article  Google Scholar 

  31. Zhang Y, Pu YF, Hu JR, Zhou JL (2012) A class of fractional-order variational image inpainting models. Appl Math Inf Sci 6(2):299–306

    MathSciNet  Google Scholar 

  32. Zhang J, Wei ZH (2011) A class of fractional-order multi-scale variational models and alternating projection algorithm for image denoising. Appl Math Model 35(5):2516–2528

    Article  MathSciNet  MATH  Google Scholar 

  33. Zhang J, Wei ZH, Xiao L (2011) Adapive fractional-order multi-scale method for image denoising. J Math Imaging Vis 43(1):39–49

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities No.106112014CDJZR188801.

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Correspondence to Shangbo Zhou.

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Yin, X., Zhou, S. & Siddique, M.A. Fractional nonlinear anisotropic diffusion with p-Laplace variation method for image restoration. Multimed Tools Appl 75, 4505–4526 (2016). https://doi.org/10.1007/s11042-015-2488-6

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  • DOI: https://doi.org/10.1007/s11042-015-2488-6

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