Infrared image enhancement algorithm based on adaptive weighted guided filter

doi:10. 19682/ j. cnki. 1005-8885. 2022. 0003

中国邮电高校学报(英文版) ›› 2022, Vol. 29 ›› Issue (2): 73-84.doi: 10. 19682/ j. cnki. 1005-8885. 2022. 0003

• Image Processing • 上一篇下一篇

Infrared image enhancement algorithm based on adaptive weighted guided filter

卢莹¹,黄世奇¹,王文庆²,孙柯¹

1. 西安邮电大学
2. 西安邮电学院

收稿日期:2021-01-28 修回日期:2021-07-09 出版日期:2022-04-26 发布日期:2022-04-26
通讯作者: 王文庆 E-mail:wwq@xupt.edu.cn
基金资助:
国家自然科学基金项目

Infrared image enhancement algorithm based on adaptive weighted guided filter

Received:2021-01-28 Revised:2021-07-09 Online:2022-04-26 Published:2022-04-26
Supported by:
This work was supported by the National Natural Science Foundation of China (61673017, 61905285), and the Shaanxi
Provincial Department of Science and Technology Key Project in the Field of Industry (2018ZDXM-GY-039).

摘要/Abstract

摘要：

The physical principle of infrared imaging leads to the low contrast of the whole image, the blurring of contour and edge details, and it is also sensitive to noise. To improve the quality of infrared image and visual effect, an adaptive weighted guided filter (AWGF) for infrared image enhancement algorithm was proposed. The core idea of AWGF algorithm is to propose an adaptive strategy to update the weights of guided filter (GF) parameters, which not only improves the accuracy of regularization parameter estimation in GF theory, but also achieves the purpose of removing infrared image noise and improving its detail contrast. A large number of real infrared images were used to verify AWGF algorithm, and good experimental results were obtained. Compared with other guided filtering algorithms, the halo phenomenon at the edge of infrared images processed by the AWGF algorithm is significantly avoided, and the evaluation parameter values of information entropy (IE), average gradient (AG), and moment of inertia (MI)are relatively high. This shows that the quality of infrared image processed by the AWGF algorithm is better.

关键词: infrared image, guided filter (GF), adaptive weight, image enhancement, regularization parameter

Abstract:

Key words: infrared image, guided filter (GF), adaptive weight, image enhancement, regularization parameter

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Infrared image enhancement algorithm based on adaptive weighted guided filter

Infrared image enhancement algorithm based on adaptive weighted guided filter

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