`Abstract and Applied AnalysisVolume 2014 (2014), Article ID 157542, 7 pageshttp://dx.doi.org/10.1155/2014/157542`
Research Article

## Theory Analysis of Left-Handed Grünwald-Letnikov Formula with to Detect and Locate Singularities

1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
2Ningbo Xingaoyi Co., Ltd., No. 555 Yeshan Road, Yuyao City, Zhejiang 315400, China

Received 21 January 2014; Accepted 9 March 2014; Published 3 April 2014

Copyright © 2014 Shaoxiang Hu and Ping Liang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

We study fractional-order derivatives of left-handed Grünwald-Letnikov formula with to detect and locate singularities in theory. The widely used four types of ideal singularities are analyzed by deducing their fractional derivative formula. The local extrema of fractional derivatives are used to locate the singularities. Theory analysis indicates that fractional-order derivatives of left-handed Grünwald-Letnikov formula with can detect and locate four types of ideal singularities correctly, which shows better performance than classical 1-order derivatives in theory.

#### 1. Introduction

How to preserve singularities in image and signal processing is a very important problem [15]. Recently, fractional derivatives become very important tools in the field [617]. All of the methods based on fractional derivatives reported good processing results by modifying some classical partial differential equations (PDEs) to fully or partial fractional-order derivative PDEs. Just as the recent theory analysis efforts in fractional derivatives by mathematicians [1820], all theory analyses focus on how to approach the PDEs and how to find their exact solutions.

However, we think fractional derivatives should be studied differently. It is well known that one powerful method to preserve singularities in signal processing is to detect and locate singularities correctly and then to protect them in signal processing. Thus, we think the most important problem in theory analysis should be if the fractional derivatives can detect and locate singularities well.

In this paper, we study fractional-order derivatives of left-handed Grünwald-Letnikov formula with to detect and locate the widely used four types of ideal singularities in theory. Theory analysis is from deducing the fractional derivatives of four types of ideal singularities with an indicated singularity in each case. The differences of these fractional derivative values are studied to find the local extrema. The extrema are considered as the singularities.

The rest of this paper is as follows. Section 2 introduces some basic theory backgrounds in fractional derivatives; we also deduce some useful results based on these theory backgrounds. In Section 3, we introduce the 1-order differential method used in singularity detection and location, and the detected and located results of four types of ideal singularities are also presented. Section 4 discusses the steps in singularities detection and location using fractional derivatives, and then they are used to detect and locate the four types of ideal singularities. We also give conclusions and acknowledgments finally.

#### 2. Fractional Derivatives

In contrast to integer-order differentials , fractional-order derivatives are defined as operators whose orders have been extended to noninteger numbers. There are a number of definitions of fractional derivatives. The usual way of representing the fractional derivatives is by the left-handed Riemann-Liouville formula (R-L formula). The left-handed R-L formula of order , for , is defined by [21, 22] where and , with denoting the integer part of , and is the Gamma function defined as

Another way to represent the fractional derivatives is by the Grünwald-Letnikov (G-L) formula, which is a generalization of the ordinary discretization formulas for integer-order derivatives. For , the left-handed G-L formula is where denotes the uniform space step and represents the normalized G-L weights that are given by

The above two definitions have different forms. However, by requiring a reasonable behavior of the function and their derivatives, we can relate the two definitions with the following proposition [21, 23].

Proposition 1. Let us assume that the function is times differential in and that the th derivative of is integrable in . Then, for every , one has

Generally, the analytic definition given by (1) is used in the formulation of the fractional partial differential equations (PDEs), while G-L definitions in (3) are maybe used to discretize the fractional PDEs to obtain a numerical solution. Since the problem in this paper has discrete form, G-L formula defined in (3) will be adopted in our following discussion.

Remark 2. For , (1) and (3) become the classical first derivative, and, for any , is a positive integer; they are classical th derivatives of if the function is times differentiable. Note that, for is a positive integer, the equations are with limit support whose support length is . However, for is not an integer, fractional derivatives defined above are nonlocal operators. That is, the value of the fractional derivative at a point depends on the function values at all the points to the left of the point .

Therefore, in order to handle fractional derivative numerically, it is necessary to compute the coefficients , where is the order of the fractional derivative. For that, we can use the recurrence relationships presented in Lemma 3.

Lemma 3. The recurrence relationship of the coefficients of G-L formula defined in (3) is

Proof. For , .
Assume that when , we have For , Therefore, for all , we have

Lemma 4. The nonlocal operator defined in (3) is a linear operator.

Proof. and are two functions, and , , is a real number, since(1)(2)

Lemma 5. Consider .

Proof. Since , let , and we have

Lemma 6. For , is a positive integer, , and one has and , .

Proof. For ,  .
Assume that .
According to Lemma 3, we have . Since and , . Thus, . Here, Then we have . Thus , .

Lemma 7. For , is an integer,

Proof. According to Lemma 5, we have According to Lemma 6, . Therefore, .

Lemma 8. For , is an integer and is a positive integer

Proof. From Lemma 6, for , , .
Thus, for , and . We have , , and , , .

#### 3. Classical Singularity Detection and Location

Singularity detection is the name for a set of mathematical methods which aim at identifying points in a digital signal at which the signal value changes sharply or, more formally, has discontinuities.

##### 3.1. Singularity Types

We can categorize singularities as step, roof, jump, and ramp. They can be represented as(1)ideal step (2)ideal roof (3)ideal impulse (4)ideal ramp

#### 4. Singularity Detection by 1-Order Derivatives

Singularity detection is the name for a set of mathematical methods which aim at identifying points in a digital signal at which the signal value changes sharply or, more formally, has discontinuities.

##### 4.1. The First-Order Derivatives for Singularities

The singularity detection by 1-order derivatives detects singularities by first computing a measure of singularity strength, usually a first-order derivative expression, and then searching for local absolute maxima as the locations of singularities. The simplest approach to compute first-order derivatives is to use left-handed differences Therefore, first-order derivatives of the four types of singularities are(1)ideal step (2)ideal roof (3)ideal impulse (4)ideal ramp Then, find absolute maxima of to locate the positions of singularities. Here, absolute maxima are the maxima of absolute values of .

##### 4.2. Detect and Locate Singularities

The main steps to locate singularities are as follows: (1) find all points with ; (2) compute absolute values of ; (3) if , compare absolute values of and , and if , is the singularity; if , compare absolute values of and , and if , is the singularity. Four types of ideal singularities are discussed as follows. Each one has a corresponding singularity on .

Ideal Step. The absolute values of are the same as . Therefore, the absolute maxima are 1 on the position , and then compare absolute values of and , and we have . Therefore, the singularity is detected by 1-order derivative and is located on , which means 1-order derivatives can detect and locate ideal step singularities correctly.

Ideal Roof. The absolute value of is Here, is For , , we should compare the absolute values of and . Since , all are not singularities.

For , , we should compare the absolute values of and . Since , all are not singularities.

, and we should compare the absolute values of and . Since and , if , is the singularity; otherwise, is not a singularity.

, and we should compare the absolute values of and . Since and , if , is the singularity; otherwise, is not a singularity.

For , , we should compare the absolute values of and . Since , are not the singularities.

For , , we should compare the absolute values of and . Since , are not the singularities.

Ideal Impulse. has two nonzeros, that is, and . For , comparing the absolute values of and , we have . Thus, is not the singularity. For , comparing the absolute values of and , we have . Thus, is the singularity.

For , comparing the absolute values of and , we have . Thus, is not the singularity. For , comparing the absolute values of and , we have . Thus, is the singularity.

In summary, for , no singularity is detected; for , both and are located as singularities.

Ideal Ramp. The absolute value of is For , , we should compare the absolute values of and . Since , all are not singularities.

For , , we should compare the absolute values of and . Since , all are not singularities.

, and we should compare the absolute values of and . Since and , is the singularity. Thus, when , ideal ramp singularity can be detected and located correctly by 1-order derivatives.

Summarizing the above conclusion, we have the following.

Theorem 9. The detection and location of four types of ideal singularities using 1-order derivatives are (1)ideal step: 1-order derivative can detect and locate ideal step singularities correctly;(2)ideal roof: 1-order derivative can detect ideal roof singularities when . But, for , a false singularity is located;(3)ideal impulse: the singularity can not be detected;(4)ideal ramp: 1-order derivative can detect and locate ideal ramp singularities correctly when , for , and the singularity can not be detected.

#### 5. Singularity Detection by Fractional-Order Derivatives

The singularity detection by fractional-order derivatives detects singularities by computing fractional-order derivative expression firstly and then searching for local extrema as the locations of singularities.

##### 5.1. Fractional Derivatives of Four Types of Singularities

Fractional-order derivatives of the four types of singularities are as follows.

Ideal Step. The fractional-order derivatives of ideal step singularities are

Ideal Roof. The fractional-order derivatives of ideal roof singularities are

Ideal Impulse. Since there are only , we have

Ideal Ramp. The fractional-order derivatives of ideal ramp singularities are

##### 5.2. Find Extremum

The main steps to find extrema of fractional-order derivatives with are as follows: for all , compare values of , , and . If and or and , where is predefined threshold, is the singularity. Four types of ideal singularities are discussed as follows. Each one has a corresponding singularity on .

Based on the above discussion, we can detect and locate four types of singularities as follows.

Ideal Step. Since , and . According to Lemma 6, for . Thus, and . So, is the singularity. When , and , so all are not singularities.

From the summary above, there is only one singularity on .

Ideal Roof. Since thus, The ideal roof implies that when , we have , , and when , we have .

According to Lemma 6, and, according to Lemma 7, . Moreover, , Thus, the right hand of (35) is and it has the same sign as . That is, when , it is a positive number while when , it is a negative number. The right hand of (36) can be written as , which also has the same sign as . Therefore, when , is a maximum while when , is a minimum. In summary, is a singularity.

Ideal Impulse. For , , , and . According to Lemma 5, and for . Thus, when , we have and , while when , we have and . Therefore, is the singularity.

Ideal Ramp. Since according to the above discussion of ideal roof, we know that has the same sign as . Thus, we only discuss as follows: According to Lemma 4, , and , we have . Therefore, has the same sign as . That is, when , we have and , and when , we have and .

Thus, is the singularity.

Summarizing the above conclusion, we have the following.

Theorem 10. The fractional derivatives can detect and locate four types of ideal singularities correctly.

#### 6. Conclusions

In this paper, we study fractional-order derivatives of left-handed Grünwald-Letnikov formula with to detect and locate singularities in theory. Theory analysis indicates that fractional-order derivatives of left-handed Grünwald-Letnikov formula with can detect and locate the ideal four types of singularities correctly, which shows better performance than classical 1-order derivatives in theory.

#### Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

#### Acknowledgments

This paper is supported by the National Natural Science Foundation of China (nos. 60873102 and 60873264), Major State Basic Research Development Program (no. 2010CB732501), and Open Foundation of Visual Computing and Virtual Reality Key Laboratory of Sichuan Province (no. J2010N03). This work was supported by a Grant from the National High Technology Research and Development Program of China (no. 2009AA12Z140).

#### References

1. Y. Chen, Y. Li, W. Yu, L. Luo, W. Chen, and C. Toumoulin, “Joint-map tomographic reconstruction with patch similarity based mixture prior model,” Multiscale Modeling and Simulation, vol. 9, no. 4, pp. 1399–1419, 2011.
2. S. Hu, Z. Liao, D. Sun, and W. Chen, “A numerical method for preserving curve edges in nonlinear anisotropic smoothing,” Mathematical Problems in Engineering, vol. 2011, Article ID 186507, 14 pages, 2011.
3. Y. Chen, X. Yin, L. Shi et al., “Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing,” Physics in Medicine and Biology, vol. 58, no. 16, pp. 5803–5820, 2013.
4. Z. Bian, J. Ma, J. Huang et al., “SR-NLM: a sinogram restoration induced non-local means image filtering for low-dose computed tomography,” Computerized Medical Imaging and Graphics, vol. 37, no. 4, pp. 293–303, 2013.
5. J. Ma, H. Zhang, Y. Gao et al., “Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior,” Physics and Medical Biology, vol. 57, no. 22, pp. 7519–7542, 2012.
6. Z. Jun and W. Zhihui, “A class of fractional-order multi-scale variational models and alternating projection algorithm for image denoising,” Applied Mathematical Modelling, vol. 35, no. 5, pp. 2516–2528, 2011.
7. J. Bai and X.-C. Feng, “Fractional-order anisotropic diffusion for image denoising,” IEEE Transactions on Image Processing, vol. 16, no. 10, pp. 2492–2502, 2007.
8. M. Janev, S. Pilipović, T. Atanacković, R. Obradović, and N. Ralević, “Fully fractional anisotropic diffusion for image denoising,” Mathematical and Computer Modelling, vol. 54, no. 1-2, pp. 729–741, 2011.
9. C. Wang, L. Lan, and S. Zhou, “Grünwald-Letnikov based adaptive fractional differential algorithm on image texture enhancing,” Journal of Computational Information Systems, vol. 9, no. 2, pp. 445–454, 2013.
10. H. A. Jalab and R. W. Ibrahim, “Texture enhancement based on the Savitzky-Golay fractional differential operator,” Mathematical Problems in Engineering, vol. 2013, Article ID 149289, 8 pages, 2013.
11. S. Hu, Z. Liao, and W. Chen, “Sinogram restoration for low-dosed X-ray computed tomography using fractional-order Perona-Malik diffusion,” Mathematical Problems in Engineering, vol. 2012, Article ID 391050, 13 pages, 2012.
12. Z. Liao, “Low-dosed x-ray computed tomography imaging by regularized fully spatial fractional-order Perona-Malik diffusion,” Advances in Mathematical Physics, vol. 2013, Article ID 371868, 9 pages, 2013.
13. Z. Liao, “A new definition of fractional derivatives based on truncated left-handedGrunwald-Letnikov formula with $0<\alpha <1$ and median correction,” Abstract and Applied Analysis. In press.
14. M. Li, “A class of negatively fractal dimensional Gaussian random functions,” Mathematical Problems in Engineering, vol. 2011, Article ID 291028, 18 pages, 2011.
15. M. Li and W. Zhao, “On bandlimitedness and lag-limitedness of fractional Gaussian noise,” Physica A: Statistical Mechanics and Its Applications, vol. 392, no. 9, pp. 1955–1961, 2013.
16. M. Li, S. C. Lim, C. Cattani, and M. Scalia, “Characteristic roots of a class of fractional oscillators,” Advances in High Energy Physics, vol. 2013, Article ID 853925, 7 pages, 2013.
17. S. Hu, “External fractional-order gradient vector Perona-Malik diffusion for sinogram restoration of low-dosed x-ray computed tomography,” Advances in Mathematical Physics, vol. 2013, Article ID 516919, 10 pages, 2013.
18. D. A. Benson, S. W. Wheatcraft, and M. M. Meerschaert, “Application of a fractional advection-dispersion equation,” Water Resources Research, vol. 36, no. 6, pp. 1403–1412, 2000.
19. R. Hilfer, Applications of Fractional Calculus in Physics, vol. 463, World Scientific, Singapore, 2000.
20. E. Sousa, “How to approximate the fractional derivative of order $1<\alpha \le 2$,” International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, vol. 22, no. 4, Article ID 1250075, 13 pages, 2012.
21. I. Podlubny, Fractional Differential Equations, Academic Press, San Diego, Calif, USA, 1999.
22. S. G. Samko, A. A. Kilbas, and O. I. Marichev, Fractional Integrals and Derivatives: Theory and Applications, Gordon and Breach, London, UK, 1993.
23. A. A. Kilbas, H. M. Srivastava, and J. J. Trujillo, Theory and Applications of Fractional Differential Equations, Elsevier, Amsterdam, The Netherlands, 2006.