The Adaptive Fractional Order Differential Model for Image Enhancement Based on Segmentation

2017 ◽  
Vol 32 (03) ◽  
pp. 1854005 ◽  
Author(s):  
Suqin Chen ◽  
Fengqun Zhao
Keyword(s):  
Image Enhancement ◽  
Fractional Order ◽  
Target Object ◽  
Optimal Order ◽  
Enhancement Effect ◽  
Differential Model ◽  
Enhancement Method ◽  
Fractional Differential ◽  
Object Area ◽  
Different Characteristics

For image enhancement method based on the fractional order differential, it is difficult to artificially give the optimal order of the fractional differential which can make the image enhancement effect better, and it is hard to ensure the enhancement of the target object while preserving the information of background pixels if the entire image is filtered by a fixed differential order. In order to solve this problem, the image is segmented into the object area and the background area according to the Otsu threshold algorithm based on Markov Random Field firstly. On the basis of the principle of the fractional differential for image enhancement, a piecewise function is established by combining with the different characteristics of pixels in each area, then the best order of fractional differential in the two areas can be determined adaptively. Thus, a novel adaptive fractional order differential algorithm for image enhancement on the basis of segmentation is put forward. Several fog-degraded traffic images are selected for experiments and processed by three other algorithms. The results of comparison exhibit the superiority of our algorithm.

scholarly journals Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

2022 ◽  
Vol 14 (1) ◽  
pp. 233
Author(s):  
Weijie Chen ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola K. Kasabov
Keyword(s):  
Image Enhancement ◽  
Spatial Domain ◽  
Multispectral Image ◽  
Multispectral Images ◽  
Dark Channel Prior ◽  
Differential Model ◽  
Dark Channel ◽  
Enhancement Method ◽  
Fractional Differential ◽  
Channel Theory

Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods.

Medical Image Enhancement Method Based on the Fractional Order Derivative and the Directional Derivative

2017 ◽  
Vol 32 (03) ◽  
pp. 1857001 ◽  
Author(s):  
Jinlan Guan ◽  
Jiequan Ou ◽  
Zhihui Lai ◽  
Yuting Lai
Keyword(s):  
Image Enhancement ◽  
Fractional Order ◽  
Medical Image ◽  
Directional Derivative ◽  
Order Derivative ◽  
Fractional Order Derivative ◽  
Texture Information ◽  
Enhancement Method ◽  
Fractional Differential ◽  
Medical Image Enhancement

In recent years, the fractional order derivative has been introduced for image enhancement. It was proved that the medical image enhancement method based on the fractional order derivative has better effect than the method based on the integral order calculus. However, a priori information such as texture surrounding a pixel is normally ignored by the traditional fractional differential operators with the same value in the eight directions. To address the above problem, this paper presents a new medical image enhancement method by taking the merits of fractional differential and directional derivative. The proposed method considers the surrounding information (such as the image edge, clarity and texture information) and structural features of different pixels, as well as the directional derivative of each pixel in constructing the masks. By proposing this method, it can not only improve the high frequency information, but also improve the low frequency information of the image. Ultimately, it enhances the texture information of the image. Extensive experiments on four kinds of medical image demonstrate that the proposed algorithm is in favor of preserving more texture details and superior to the existing fractional differential algorithms on medical image enhancement.

scholarly journals The Fractional Differential Model of HIV-1 Infection of CD4+T-Cells with Description of the Effect of Antiviral Drug Treatment

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Bijan Hasani Lichae ◽  
Jafar Biazar ◽  
Zainab Ayati
Keyword(s):  
T Cells ◽  
Fractional Order ◽  
Fractional Derivatives ◽  
Antiviral Drug ◽  
Adomian Decomposition Method ◽  
Differential Model ◽  
Proposed Model ◽  
Adomian Decomposition ◽  
Fractional Differential ◽  
Hiv 1

In this paper, the fractional-order differential model of HIV-1 infection of CD4+T-cells with the effect of drug therapy has been introduced. There are three components: uninfected CD4+T-cells,x, infected CD4+T-cells,y, and density of virions in plasma,z. The aim is to gain numerical solution of this fractional-order HIV-1 model by Laplace Adomian decomposition method (LADM). The solution of the proposed model has been achieved in a series form. Moreover, to illustrate the ability and efficiency of the proposed approach, the solution will be compared with the solutions of some other numerical methods. The Caputo sense has been used for fractional derivatives.

Medical Image Enhancement Method Based on Wavelet Transform and Mode Decomposition

2013 ◽  
Vol 389 ◽  
pp. 930-935 ◽  
Author(s):  
Ao Shuang Dong ◽  
Bin Bin Lou ◽  
Hui Yan Jiang ◽  
Qiang Tong ◽  
Guang Ming Yang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
Empirical Mode Decomposition ◽  
Medical Image ◽  
Enhancement Effect ◽  
Mode Decomposition ◽  
Enhancement Method ◽  
Image Edge ◽  
Bidimensional Empirical Mode Decomposition ◽  
Medical Image Enhancement

Traditional medical image enhancement method has some disadvantages. They can not significantly improve the medical image edge, texture and detailed information. Besides the enhancement effect is susceptible to interference noise information. This paper proposed enhancement algorithms combining bidimensional empirical mode decomposition and the wavelet edge enhancement method. The first step is using the method of bidimensional empirical mode decomposition to process medical image, achieve image information with different frequency. And then our method using wavelet transform to enhance different frequency image edge, texture information. Through the comparison of proposed method with the existing method, it has been verified the proposed method has a better effect in the detail enhancement of medical images.

Image Enhancement Based on Fractional Differential and Image Entropy

2010 ◽  
Vol 159 ◽  
pp. 232-235
Author(s):  
Ya Wei Liu ◽  
Jian Wei Li
Keyword(s):  
Mutual Information ◽  
Image Enhancement ◽  
Experimental Results ◽  
Fractional Operator ◽  
Selection Differential ◽  
Image Entropy ◽  
Enhancement Method ◽  
Fractional Differential ◽  
The Difference

In this paper, a new image enhancement method is proposed based on fractional differential, which can select the differential order automatically by the difference of mutual information (DMI). DMI describes the increase of mutual information in original and enhancement image. Being a measure of ascertaining the ehancement effect, it is considered getting the information balance in the images processed by different differential order. According to it, a criterion of selection differential order is put forward. Image convolutions are implemented with fractional operator in different scales, and then DMI of adjacent scales are calculated. The differential order can be selected in which the DMI is the mininum. The experimental results indicate that the proposed method is effective, and has better result compared with other methods.

scholarly journals A Fractional Epidemiological Model for Bone Remodeling Process

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Muath Awadalla ◽  
Yves Yannick Yameni Noupoue ◽  
Kinda Abuasbeh
Keyword(s):  
Bone Formation ◽  
Differential Equations ◽  
Fractional Order ◽  
Formation Process ◽  
Differential Model ◽  
Chemical Substances ◽  
Differential Approach ◽  
Fractional Differential ◽  
The Impact ◽  
Calcitonin Secretion

This article focuses on modeling bone formation process using a fractional differential approach, named bones remodeling process. The first goal of the work is to investigate existence and uniqueness of the proposed fractional differential model. The next goal is to investigate how similar is the proposed approach to the method based on system classical differential equations. The dynamical system of equations used is built upon three main parameters. These are chemical substances, namely, calcitonin secretion, osteoclastic and osteoblastic, which are involved in the bone’s formation process. We implement some numerical simulations to graphically show the impact of an arbitrary fractional order of derivative. We finally obtained that modeling bone formation process using fractional differential equations yielded comparable results with those obtained through a system of classical differential equations. Flexibility in the choice of the fractional order of derivative is an advantage as it helps in selecting the best fractional order of derivative.

Fractional Differential-Based Approach for CT Image Enhancement

2013 ◽  
Vol 634-638 ◽  
pp. 3962-3965 ◽  
Author(s):  
Da Li Chen ◽  
Ding Yu Xue ◽  
Yang Quan Chen
Keyword(s):  
Image Enhancement ◽  
Fractional Order ◽  
Ct Image ◽  
One Dimensional ◽  
Feature Enhancement ◽  
Fractional Differential ◽  
Image Enhancing ◽  
The One ◽  
Texture Preservation

In this paper, a fractional differential-based approach for CT image enhancement is introduced. This approach uses a group of fractional differential masks, which are generalized from the one-dimensional digital fractional order Savitzky-Golay differentiator, to process the image and a max-saturation strategy is designed to enhance these processed images. Some experiments are used to assess the performance of the proposed fractional differential-based image enhancing algorithm, and the results demonstrate that the proposed enhancing method is able to achieve a good tradeoff between the feature enhancement and texture preservation.

An Image Enhancement Method in Mixed Space

2013 ◽  
Vol 433-435 ◽  
pp. 400-404
Author(s):  
Qun Li Li ◽  
Li Sha Li ◽  
Feng Xiao
Keyword(s):  
Image Enhancement ◽  
Laplace Operator ◽  
Gradient Method ◽  
Enhancement Effect ◽  
Laplace Method ◽  
Sobel Operator ◽  
Enhancement Method ◽  
Spatial Method ◽  
The Laplace Operator ◽  
Better Than

An image enhancement method in mixed space is proposed in this paper, it combines the Laplace operator and the Sobel operator, gives full play the advantages of the two algorithms. It shows that the image enhancement effect of the mixed spatial method is better than the Laplace method and gradient method, through the enhancement experiments to the same image in the three ways, comparing and analyzing the results of their treatment.

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