Image enhancement of adaptive fractional operator

Li Shuai; Wang Weiming; Liu Xianhong; Yan Deli

doi:10.12086/oee.2019.180517

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Li Shuai, Wang Weiming, Liu Xianhong, et al. Image enhancement of adaptive fractional operator[J]. Opto-Electronic Engineering, 2019, 46(9): 180517. doi: 10.12086/oee.2019.180517

Citation:

Li Shuai, Wang Weiming, Liu Xianhong, et al. Image enhancement of adaptive fractional operator[J]. Opto-Electronic Engineering, 2019, 46(9): 180517. doi: 10.12086/oee.2019.180517

Image enhancement of adaptive fractional operator

1.
School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang, Hebei 050043, China
2.
Unit 32142 of PLA, Shijiazhuang, Hebei 050000, China

Fund Project: Supported by National Natural Science Foundation of China (11372199) and Natural Science Foundation of Hebei (E2016210104)

More Information

^*Corresponding author: Wang Weiming, E-mail:wangwm@stdu.edu.cn

Received Date 09 October 2018

Revised Date 26 November 2018

Published Date 30 September 2019

Abstract

Abstract

In order to highlight the texture details of the image while preserving the smooth region and saving the time to determine the fractional differential order, an improved adaptive fractional differential operator is proposed. Firstly, the classical Tiansi template is decomposed into four different directions, which are respectively convolved with the pixels to be processed to achieve the effect of enhancing the texture details of the image. Secondly, the current situation of the optimal differential order is determined by the experiment for the Tiansi operator. The local feature information of the image constructs a fractional order model with an adaptive ability, which can obtain more detailed information than the original image. The experimental results of multiple sets of different scene images show that the constructed adaptive fractional differential operators effectively enhance the texture details of the image. The subjective visual effects and objective evaluation indexes of the adaptive fractional differential operators are better than the original images. The average gradient, information entropy and contrast in the objective evaluation index are increased by 190.3%, 8.1%, and 18.3%, respectively. The average gradient and contrast are 45.0% and 9.6% higher than that of the Tiansi operator.
- image enhancement /
- fractional differential /
- Tiansi operator /
- adaptive fractional

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References

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Overview

Overview

Overview: Image processing technology has become increasingly prominent in the fields of intelligent transportation and video surveillance. Visibility is relatively low in foggy weather and nighttime scenes, and images collected by equipment often have severe degradation and distortion. Therefore, it is especially important to study how to improve the quality of video images in bad weather. In the image enhancement processing, in order to improve the visual effect of the image, it is desirable that the high-frequency component of the image can be enhanced to highlight the texture information of the image while further smoothing the low-frequency component of the image. The linear transformation of the image has the advantage of smoothing the low-frequency components of the image, but such a method retains too little high-frequency components to achieve the desired effect. When the image is averaged or integrated, blurring occurs. In order to make the edge contour extending in any direction in the image clearer, the image can be inversely operated, such as differential operation. The first-order differential gradient operator and the second-order differential Laplacian operator have the advantage of not only enhancing high-frequency components of the image and highlighting texture information in the image, but also increasing the image noise. The traditional image enhancement processing can not solve the contradiction of removing image noise while enhancing the texture details of the image.

In recent years, fractional calculus has made breakthroughs in many fields. It has been found that fractional differential operators have the property of weak derivatives, and more and more people apply them to the field of image processing. In order to highlight the texture details of the image while preserving the smooth region and saving the time to determine the fractional differential order, an improved adaptive fractional differential operator is proposed. Firstly, the classical Tiansi template is decomposed into four different directions, which are respectively convolved with the pixels to be processed to achieve the effect of enhancing the texture details of the image. Secondly, the current situation of the optimal differential order is determined by the experiment for the Tiansi operator. The local feature information of the image constructs a fractional order model with an adaptive ability, which can obtain more detailed information than the original image. The experimental results of multiple sets of different scene images show that the constructed adaptive fractional differential operators effectively enhance the texture details of the image. The subjective visual effects and objective evaluation indexes of the adaptive fractional differential operators are better than the original images. The average gradient, information entropy and contrast in the objective evaluation index are increased by 190.3%, 8.1%, and 18.3%, respectively. The average gradient and contrast are 45.0% and 9.6% higher than that of the Tiansi operator.