# A simple and flexible modification of Grünwald–Letnikov fractional derivative in image processing

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## Abstract

In image processing, edge detection and image enhancement can make use of fractional differentiation operators, especially the Grünwald–Letnikov derivative. In this paper, we present a modified Grünwald–Letnikov derivative to enhance more and detect better the edges of an image. Our proposed fractional derivative is very flexible and can be easily performed. We present some examples to justify our suggested approach.

## Keywords

Grünwald–Letnikov fractional derivative Image enhancement Edge detection## Introduction

The fractional differential equation has a long history of more than 300 years. Many mathematicians such as Euler, Laplace, Abel, Liouville, Riemann, Grünwald, Letnikov and Riez have worked in this field of mathematics. In 1974, first conference on fractional calculus and its application was held [1]. In Podlubny [2] wrote a book that provides the basic theory of fractional differentiation, equations and methods of their solution. Models based on partial differential equations and calculus of variations are also generalized for fractional derivatives. For instance, fractional-order partial differential equation-based formulation are applicable for multi-scale nonlocal contrast enhancement with texture preserving [3] and iterative learning control with high-order internal models [4]. In image processing, fractional calculus is exploited in image denoising using the diffusion equation [5, 6, 7, 8] and in image segmentation with active contours using the fractional derivative within energy functional [9]. Mathieu et al. [10] applied the fractional differentiation for edge detection. Also, they discussed on the texture enhancement of multi-scale fractional mask.

Zhang et al. [11] have proposed fractional differential mask based on the definition of Riemann–Liouville. For fractional order of 1 to 2, they enhanced the texture and edges in multi-scale by controlling the fractional order. For denoising an image, Pu et. al applied fractional calculus based on the definition of Riemann–Liouville [12]. Also, Gao et al. in [13] applied an improved fractional differential operator based on a piecewise quaternion for image enhancement. Furthermore, in [14], the generalized fractional image denoising algorithm based on Srivastava–owa fractional differential operator is introduced for image denoising. The Grünwald–Letnikov derivative is also used for image enhancement in [15, 16]. In Gao et al. [17] by development of the real fractional derivative and its applications in the signal processing extended the quaternion fractional differential (QFD) based on Grünwald–Letnikov and applied it to edge detection of color image. He et al. in [18] proposed a model based on the Grünwald–Letnikov fractional differential operator that improves denoising operator mask. The total coefficient of this mask is not equal to zero, which means that its response value is not zero in flat areas of the image. The total coefficient of this mask is not equal to zero, which means that its response value is not zero in flat areas of the image. In 2017, Jalab et al. proposed a new contrast enhancement technique for medical images based on image entropy. Their method enhances edges accurately while preserving smooth textures [19]. We aim to redefine the Grünwald–Letnikov derivative, in order to better show the rate of changes of the derivative in image processing. In this paper, we highlight the defects of Grünwald–Letnikov derivative in image processing and based on them, we present a new definition of Grünwald–Letnikov derivative that is very flexible.

## Preliminaries

*n*th-order derivative of function

*f*is defined by:

*f*is defined as follows [2]:

*f*(

*x*,

*y*) where

*x*and

*y*are spatial coordinates. The value of

*f*(

*x*,

*y*) is called the color intensity of image at point (

*x*,

*y*). In the field of image processing, the Grünwald–Letnikov derivative in two dimensions in the x-direction can be defined as follows [15, 20]:

- 1.
For the region of an image I whose color intensities are the same, the gradient of I is zero inside of the region(not on the edge points), but it is nonzero for Grünwald–Letnikov derivative. Furthermore, the more the intensity is closer to white (255), the larger the Grünwald–Letnikov derivative.

- 2.
In edge pixels that gradient is positive (negative), the Grünwald–Letnikov derivative is also positive (negative). However, the (absolute) value of Grünwald–Letnikov derivative is usually larger than that of regular gradient.

### Example 1

### Example 2

In Examples 1 and 2, the value of *f*(*x*, *y*) is constant in the x-neighborhood of *f*(*x*, *y*), hence, we expect no change or a few change of (fractional) derivative of *f* in x-direction. However, we see the value of \( D^\alpha _{G-L}f_x(x,y)\) severely depends on the intensity of *f* rather than the difference of *f* and their x-neighborhoods.

### Example 3

### Example 4

In Examples 3 and 4, we observe that the difference of *f*(*x*, *y*) and its x-neighborhoods are the same; however, the Grünwald–Letnikov derivatives of *f*(*x*, *y*) in x-direction are very different. The above examples show that Grünwald–Letnikov derivative is sensitive to the intensity of the pixels rather than the difference of the intensities.

According to these examples, the definition of Grünwald–Letnikov derivative should be modified in order to better represent the rate of changes of the derivative.

## Modified Grünwald–Letnikov derivative

*M*(

*x*,

*y*)) and (

*s*, 0) is

*Y*(

*x*,

*y*) is obtained. Now, we define the modified Grünwald–Letnikov derivative in x-direction as follows:

*Y*(

*x*,

*y*) to avoid of vanishing the denominator. The coefficient \(\frac{1}{Y(x,y)+1}\) is thought of as modifier parameters of Grünwald–Letnikov derivative. Moreover, it is important to note that for \(0<n\le 1\),

### Lemma 1

*The modified* *Grünwald–Letnikov derivative defined by* (4) *will be the same* *Grünwald–Letnikov as defined by* (1), if \(s\rightarrow +\infty \).

*s*and

*n*, we have two degree of freedom. In fact, the modified Grünwald–Letnikov derivative generally has a behavior between the regular derivative and Grünwald–Letnikov fractional derivative. Analogously, one can define the modified Grünwald–Letnikov derivative in y-direction. Hence, the modified Grünwald–Letnikov fractional derivative can be defined by

We observe that the multiplier \(\frac{1}{Y(x,y)+1}\) in the modified Grünwald–Letnikov derivative moderates the value of the derivative.

## Numerical examples

### Example 5

(Edge detection). Consider Fig. 1a as an original image. Figure 1b shows its Grünwald–Letnikov derivative defined by (3) and Fig. 1c shows its modified Grünwald–Letnikov derivative defined by (7). In both Fig. 1b, c, we put \(\alpha =0.5\). Also, for modified Grünwald–Letnikov derivative, \(s=255\) and \(n=0.5\) is selected. As it is seen the modified Grünwald–Letnikov derivative shows only the edges of the main figure while Grünwald–Letnikov derivative shows the whole of figure with low intensity. Based on Lemma 1, as *s* tends to infinity, the Grünwald–Letnikov derivative and its modified will be the same.

### Example 6

(Image enhancement). Figure 2 shows a gray-scale image of an infant. Figures 3 and 4 show the enhanced images of Fig. 2 by Grünwald–Letnikov derivative and modified Grünwald–Letnikov derivative with \(\alpha =0.2,0.4, 0.6\) and \(\alpha =0.8\), respectively. We considered \(s=255\) and \(n=0.5\) for enhancing by modified Grünwald–Letnikov derivative. As it is seen, the modified Grünwald–Letnikov derivative gives a better quality in comparison with the usual Grünwald–Letnikov derivative.

### Example 7

Figure 5a shows an original image of Lena, and Fig. 5b shows a regular derivative of it. It is computed as \(\sqrt{(\frac{\partial u}{\partial x})^2+(\frac{\partial u}{\partial y})^2}\) where *u* is the image of Lena. Figures 6 and 7 show the effect of Grünwald–Letnikov derivative and its modified for \(\alpha =0.2, 0.4, 0.6\) and \(\alpha =0.8\), respectively. For modified \(G-L\) derivative, we considered \(s=255\) and \(n=0.5\). It is clear that the modified \(G-L\) derivatives tends to regular \(G-L\) derivatives, as *s* tends to infinity.

## Conclusion

In order to better show the rate of change of derivative in image processing, we need to redefine the Grünwald–Letnikov fractional derivative. We highlight the defects of the Grünwald–Letnikov derivative in image processing, next, we present a new definition of Grünwald–Letnikov fractional derivative that is very flexible. The proposed modified Grünwald–Letnikov can be efficiently employed in different areas of image processing such as image enhancement, edge detection and medical diagnostic.

## Notes

## References

- 1.Ross, B. (ed.): Fractional Calculus and Its Applications, Lecture Notes in Mathematics, vol. 457. Springer, Berlin (1975)Google Scholar
- 2.Podlubny, I.: Fractional Differential Equations. Academic Press, New York (1999)zbMATHGoogle Scholar
- 3.Pu, Y.F., Siarry, P., Chatterjee, A., Wang, Z.N., Yi, Z., Liu, Y., Zhou, J., Wang, Y.: A fractional-order variational framework for retinex: fractional-order partial differential equation-based formulation for multi-scale nonlocal contrast enhancement with texture preserving. IEEE Trans. Image Process.
**27**(3), 1214–1229 (2018)MathSciNetCrossRefGoogle Scholar - 4.Liu, Sh, Wang, J.: Analysis of iterative learning control with high-order internal models for fractional differential equations. J. Vib. Control
**24**(6), 1145–1161 (2018)MathSciNetCrossRefGoogle Scholar - 5.Abrirami, A., Prakesh, P., Thangavel, K.: Fractional diffusion equation-based image denoising model using CN-GL scheme. Int. J. Comput. Math.
**95**(6–7), 1222–1239 (2018)MathSciNetCrossRefGoogle Scholar - 6.Bai, J., Feng, X.-C.: Fractional-order anisotropic diffusion for image denoising. IEEE Trans. Image Process.
**16**(10), 2492–2502 (2007)MathSciNetCrossRefGoogle Scholar - 7.Verma, A.K., Saini, B.S.: Forward-backward processing technique for image denoising using FDZP 2D filter. J. Appl. Res. Technol.
**15**, 538–592 (2018)Google Scholar - 8.Zhang, W., Li, J., Yang, Y.: A fractional diffusion-wave equation with non-local regularization for image denoising. Signal Process.
**103**, 6–15 (2014)CrossRefGoogle Scholar - 9.Ren, Z.: Adaptive active contour model driven by fractional order fitting energy. Signal Process.
**117**, 138–150 (2015)CrossRefGoogle Scholar - 10.Mathieu, B., Melchior, P., Oustaloup, A., Ceyral, Ch.: Fractional differentiation for edge detection. Signal Process.
**83**(11), 2421–2432 (2003)CrossRefzbMATHGoogle Scholar - 11.Zhang, Y., Pu, Y., Zhou, J.: Construction of fractional differential masks based on Riemann–Liouville definition. J. Comput. Inf. Syst.
**6**(10), 3191–3199 (2010)Google Scholar - 12.Hu, J., Pu, Y., Zhou, J.: A novel image denoising algorithm based on Riemann–Liouville definition. J. Comput.
**6**(7), 1332–1338 (2011)Google Scholar - 13.Gao, C.B., Zhou, J.L., Zheng, X.Q., Lang, F.N.: Image enhancement based on improved fractional differentiation. J. Comput. Inf. Syst.
**7**(1), 257–264 (2011)Google Scholar - 14.Jalab, H.A., Ibrahim, R.W.: Fractional masks based on generalized fractional differential operator for image denoising, world academy of science, engineering and technology. Int. J. Comput. Inf. Sci. Eng.
**7**(2), 124–129 (2013)Google Scholar - 15.Pu, Y.F., Zhou, J.L., Yuan, X.: Fractional differential mask: a fractional differential-based approach for multiscale texture enhancement. IEEE Trans. Image Process.
**19**(2), 491–511 (2010)MathSciNetCrossRefzbMATHGoogle Scholar - 16.Pu, Y.: Fractional calculus approach to texture of digital image. In: IEEE Proceeding of the 8th International Conference on Signal Processing, pp. 1002–1006 (2006)Google Scholar
- 17.Gao, C.B., Zhou, J.L., Hu, J.R., Lang, F.N.: Edge detection of colour image based on quaternion fractional differential. IET Image Process.
**5**(3), 261–272 (2011)MathSciNetCrossRefGoogle Scholar - 18.He, N., Wang, J.B., Zhang, L.L., Lu, K.: An improved fractional-order differentiation model for image denoising. Signal Process.
**112**, 180–188 (2015)CrossRefGoogle Scholar - 19.Jalab, H.A., Ibrahim, R.W., Jalab, D.H., Jalab, A.A., Hasan, A.M.: Medical image enhancement based on statistical distributions in fractional calculus. In: Computing Conference (2017)Google Scholar
- 20.Huading, J., Pu, Y.: Fractional calculus method for enhancing digital image of bank slip. Proc. Congr. Image Signal Process.
**3**, 326–330 (2008)Google Scholar - 21.Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, New Jersey (2007)Google Scholar

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