Abstract
Edge detection is an important aspect of image processing to improve image edge quality. In the literature, there exist various edge detection techniques in spatial and frequency domains that use integer-order differentiation operators. In this paper, we have implemented feature and contrast enhancement of image using Riemann–Liouville fractional differential operator. Based on the direction of strong edge, we have evaluated edge components and carried out a performance analysis based on several well-known metrics. We have also improved the pixel contrast based on foreground and background gray level. Moreover, by theoretical and experimental results, it is observed that the proposed feature and contrast enhancement outperforms the existing methods under comparison. We have discussed that the edge components calculated using fractional derivative can be used for texture and contrast enhancement. This paper is based on fractional-order differentiation operation to detect edges with the help of the directional edge components across eight directions. The experimental comparison results are shown in tabular form and as qualitative texture results. The six experimental input images are used to analyze various performance metrics. The experiments show that for any grayscale image the proposed method outperforms classical edge detection operators.








Similar content being viewed by others
References
K.G. Alhinai, M.A. Khan, A.A. Canas, Enhancement of sand dune texture from landsat imagery using difference of Gaussian filter. Int. J. Remote Sens. 12, 1063–1069 (2008)
H. Brunner, L. Ling, M. Yamamoto, Numerical simulations of 2D fractional subdiffusion problems. J. Comput. Phys. 229(18), 6613–6622 (2010)
Q. Chen, Z. Song, J. Dong, Z. Huang, Y. Hua, S. Yan, Contextualizing object detection and classification. IEEE Trans. Pattern Anal. Mach. Intell. 37(1), 13–27 (2015)
W. Chen, S. Holm, Fractional Laplacian time–space models for linear and nonlinear lossy media exhibiting arbitrary frequency dependency. J. Acoust. Soc. Am. 115(4), 1424–1430 (2004)
L.S. Davis, A survey of edge detection techniques. Comput. Graph. Image Process. 4(3), 248–260 (1975)
K. Diethelm, The Analysis of Fractional Differential Equations. Lecture Notes in Mathematics (Springer, Berlin, 2010)
L. Ding, A. Goshtasby, On the canny edge detector. Pattern Recogn. 34(3), 721–725 (2001)
A. Ghaffari, E. Fatemizadeh, RISM: single-modal image registration via rank-induced similarity measure. IEEE Trans. Image Process. 24(12), 5567–5580 (2015)
R.C. Gonzalez, R.E. Woods, Digital Image Processing (Prentice-Hall, Englewood Cliffs, 2008)
M. Hadwiger, J.M. Kniss, R.C. Salama, D. Weiskopf, K. Engel, Real-Time Volume Graphics (A. K. Peters Ltd., Natick, 2006)
F. He, S. Wang, Beyond \(\chi \)2 difference: learning optimal metric for boundary detection. IEEE Signal Process. Lett. 22(1), 40–44 (2015)
M. Jourlin, J.C. Pinoli, Image dynamic range enhancement and stabilization in the context of the logarithmic image processing model. Sig. Process. 41(2), 225–237 (1995)
Z. W. Ju, J.Z. Chen, J.L. Zhou, Image segmentation based on edge detection using K-means and an improved ant colony optimization. International Conference on Machine Learning and Cybernetics (ICMLC), China, pp. 297–303 (2013)
S. Kumar, R. Saxena, K. Singh, Fractional Fourier transform and fractional-order calculus-based image edge detection. Circuits Syst. Signal Process. 36(4), 1493–1513 (2017)
R. Larsen, M.B. Stegmann, S. Darkner, S. Forchhammer, T.F. Cootes, B.K. Ersboll, Texture enhanced appearance models. Comput. Vis. Image Underst. 106(1), 20–30 (2007)
M. Lehtomäki, A. Jaakkola, J. Hyyppä, J. Lampinen, H. Kaartinen, A. Kukko, E. Puttonen, H. Hyyppä, Object classification and recognition from mobile laser scanning point clouds in a road environment. IEEE Trans. Geosci. Remote Sens. 54(2), 1226–1239 (2016)
C. Lopez-Molina, H. Bustince, B. De Baets, Separability criteria for the evaluation of boundary detection benchmarks. IEEE Trans. Image Process. 25(3), 1047–1055 (2016)
S.K. Maji, H.M. Yahia, H. Badri, Reconstructing an image from its edge representation. Digit. Signal Proc. 23(6), 1867–1876 (2013)
S. Manabe, A suggestion of fractional-order controller for flexible spacecraft attitude control. Nonlinear Dyn. 29(1), 251–268 (2002)
M.D. Ortigueira, Fractional Calculus for Scientists and Engineers. Lecture Notes in Electrical Engineering (Springer, Berlin, 2011)
W.K. Pratt, Digital Image Processing, 3rd edn. (Wiley, New York, 2001)
J.M.S. Prewitt, Object Enhancement and Extraction, Picture processing and Psychopictorics (Academic Press, Cambridge, 1970), pp. 75–149
Y. Pu, J. Zhou, X. Yuan, Fractional differential mask: a fractional differential-based approach for multiscale texture enhancement. IEEE Trans. Image Process. 19(2), 491–511 (2010)
L.G. Roberts, Machine Perception of Three-Dimensional Solids. Thesis (Ph. D.) Massachusetts Institute of Technology, Department of Electrical Engineering (1963)
J. Sabatier, O.P. Agrawal, J.A.T. Machado, Advances in Fractional Calculus: Theoretical Developments and Applications in Physics and Engineering (Springer, New York, 2007)
K. Singh, R. Saxena, S. Kumar, Caputo-based fractional derivative in fractional Fourier transform domain. IEEE J. Emerg. Sel. Top. Circuits Syst. 3(3), 330–337 (2013)
I. Sobel, G. Feldman, A 3 \(\times \) 3 Isotropic Gradient Operator for Image Processing. Stanford Artificial Intelligence Project (SAIL) (1968)
F. Taponecco, T. Urness, V. Interrante, Directional enhancement in texture-based vector field visualization. 4th International Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast Asia, Malaysia, pp. 197–204 (2006)
C. Telke, M. Beitelschmidt, Edge detection based on fractional order differentiation and its application to railway track images. Proc. Appl. Math. Mech. 15, 671–672 (2015)
J. Wang, Y. Ye, X. Gao, Fractional 90-degree phase-shift filtering based on the double-sided Grunwald–Letnikov differintegrator. IET Signal Proc. 9(4), 328–334 (2015)
J. Wang, Y. Ye, Y. Gao, S. Qian, X. Gao, Fractional compound integral with application to ECG signal denoising. Circuits Syst. Signal Process. 34(6), 1915–1930 (2015)
J. Wang, Y. Ye, X. Pan, X. Gao, C. Zhuang, Fractional zero-phase filtering based on the Riemann–Liouville integral. Sig. Process. 98(5), 150–157 (2014)
D. Zosso, X. Bresson, J.P. Thiran, Geodesic active fields—a geometric framework for image registration. IEEE Trans. Image Process. 20(5), 1300–1312 (2011)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nandal, A., Gamboa-Rosales, H., Dhaka, A. et al. Image Edge Detection Using Fractional Calculus with Feature and Contrast Enhancement. Circuits Syst Signal Process 37, 3946–3972 (2018). https://doi.org/10.1007/s00034-018-0751-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00034-018-0751-6