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Research Article |

Fractional-order Diffusion based Image Denoising Model

Author(s): Sridevi Gamini1, Vishnu Vardhan Gudla2 and Ch Hima Bindu3

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 4

Publisher : FOREX Publication

Published : 18 October 2022

e-ISSN : 2347-470X

Page(s) : 837-842

DOI: https://doi.org/10.37391/IJEER.100413

Abstract

Edge indicating operators such as gradient, mean curvature, and Gauss curvature-based image noise removal algorithms are incapable of classifying edges, ramps, and flat areas adequately. These operators are often affected by the loss of fine textures. In this paper, these problems are addressed and proposed a new coefficient of diffusion for noise removal. This new coefficient consists of two edge indicating operators, namely fractional-order difference curvature and fractional-order gradient. The fractional-order difference curvature is capable of analyzing flat surfaces, edges, ramps, and tiny textures. The fractional-order gradient can able to distinguish texture regions. The selection of the order is more flexible for the fractional order gradient and fractional-order difference curvature. This will result in effective image denoising. Since the discrete Fourier transform is simple to numerically implement, it is taken into consideration for the implementation of fractional-order gradient. The proposed method can give results that are visually appealing and improved quantitative outputs in terms of the Figure of Merit (FoM), Mean Structural Similarity (MSSIM), and Peak Signal to Noise Ratio (PSNR), according to comparative analysis.

Keywords: Anisotropic Diffusion, Difference Curvature, Fourier Transform, Fractional Derivative, Image Denoising




Sridevi Gamini*, Department of Electronics and Communication Engineering, Aditya Engineering College, Surampalem, India; Email: sridevi_gamini@yahoo.com

Vishnu Vardhan Gudla, Department of Electronics and Communication Engineering, Aditya Engineering College, Surampalem, India

Ch Hima Bindu, Department of Electronics and Communication Engineering, QIS College of Engineering & Technology, Ongole, India

    [1] Golbaghi, F. K., Rezghi, M., and Eslahchi, M. 2020. A hybrid image denoising method based on integer and fractional-order total variation. Iranian Journal of Science and Technology Transaction A-science. 44, 1803– 1814. [Cross Ref]
    [2] Yin, X., Chen, S., Wang, L., and Zhou, S. 2019. Fractional-order difference curvature-driven fractional anisotropic diffusion equation for super-resolution. International Journal of Modeling, Simulation, and Scientific Computing. 10, 1941012. [Cross Ref]
    [3] Sridevi, G. and Kumar, S. S. 2019. Image inpainting based on fractional-order nonlinear diffusion for image reconstruction. Circuits, Systems, and Signal Processing. 38, 3802–3817. [Cross Ref]
    [4] Sridevi, G. and Kumar, S. S. 2017. Image inpainting and enhancement using fractional order variational model. Defence Science Journal. 67, 308–315. [Cross Ref]
    [5] Sridevi, G. and Kumar, S. S. 2017. p-Laplace Variational Image Inpainting Model Using Riesz Fractional Differential Filter. International Journal of Electrical and Computer Engineering. 7, 850–857. [Cross Ref]
    [6] Yin, X. and Zhou, S. 2015. Image structure-preserving denoising based on difference curvature driven fractional nonlinear diffusion. Mathematical Problems in Engineering. 2015. [Cross Ref]
    [7] Bai, J. and Feng, X. C. 2007. Fractional-order anisotropic diffusion for image denoising. IEEE Transactions on Image Processing. 16, 2492–2502. [Cross Ref]
    [8] Marius, L. and Tai, X. C. 2006. Iterative image restoration combining total variation minimization and a second-order functional. International journal of computer vision. 66, 5–18.[Cross Ref]
    [9] Lee, S. H. and Seo, J. K. 2005. Noise removal with gauss curvature-driven diffusion. IEEE Transactions on Image Processing. 14, 904–909. [Cross Ref]
    [10] Zhou, W., Alan, B. C., Sheikh, H. R., and Simoncelli, E. P. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing. 13, 600–612. [Cross Ref]
    [11] Tony, C. F. and Jianhong, S. 2001. Non-texture inpainting by curvature-driven diffusions. Journal of Visual Communication and Image Representation. 12, 436–449. [Cross Ref]
    [12] You, Y. L., and Kaveh, M. 2000. Fourth-order partial differential equations for noise removal. IEEE Transactions on Image Processing. 9, 1723–1730. [Cross Ref]
    [13] Rudin, L. I., Osher, S., and Fatemi, E. 1992. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena. 60, 259–268. [Cross Ref]
    [14] Perona, P. and Malik, J. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence. 12, 629–639. [Cross Ref]
    [15] Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4), 600-612.[Cross Ref]
    [16] Abdou, I. E. and Pratt, W. K. 1979. Quantitative design and evaluation of enhancement/thresholding edge detectors. Proceedings of the IEEE. 67, 753–763. [Cross Ref]
    [17] Avadhesh Kumar Dixit, Rakesh Kumar Yadav and Ramapati Mishra (2021), Contrast Enhancement of Colour Images by Optimized Fuzzy Intensification. IJEER 9(4), 143-149. DOI: 10.37391/IJEER.090408.[Cross Ref]
    [18] M. Bahadoranet al. (2014), "Detection of Salmonella bacteriumin drinking water using microring resonator," Artificial Cells, Nanomedicine, and Biotechnology, vol. 44, no. 1, pp. 315-321. [Cross Ref]

Sridevi Gamini, Vishnu Vardhan Gudla and Ch Hima Bindu (2022), Fractional-order Diffusion based Image Denoising Model. IJEER 10(4), 837-842. DOI: 10.37391/IJEER.100413.