Enhancement algorithm of low illumination image for UAV images inspired by biological vision

被引：0
|
作者：
Wang D. [1 ]
Liu W. [1 ]
Fang J. [1 ]
Xu Z. [2 ]
机构：
[1] School of Telecommunication and Information Engineering, Xi′an University of Posts and Telecommunications, Xi′an
[2] School of Computing and Engineering, University of Huddersfield, Huddersfield
来源：
Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University | 2023年 / 41卷 / 01期
关键词：
biological vision; enhancement of low illumination image; generative adversarial network; UAV images;
D O I：
10.1051/jnwpu/20234110144
中图分类号：
学科分类号：
摘要：
To address the issue of low brightness, high noise and obscure details of UAV aerial low⁃light images, this paper proposes an UAV aerial low⁃light image enhancement algorithm based on dual⁃path inspired by the dualpath model in human vision system. Firstly, a U⁃Net network based on residual element is constructed to decompose UAV aerial low⁃light image into structural path and detail path. Then, an improved generative adversarial network (GAN) is proposed to enhance the structural path, and edge enhancement module is added to enhance the edge information of the image. Secondly, the noise suppression strategy is adopted in detail path to reduce the influence of noise on image. Finally, the output of the two paths is fused to obtain the enhanced image. The experimental results show that the proposed algorithm visually improves the brightness and detail information of the image, and the objective evaluation index is better than the other comparison algorithms. In addition, this paper also verifies the influence of the proposed algorithm on the target detection algorithm under low illumination conditions, and the experimental results show that the proposed algorithm can effectively improve the performance of the target detection algorithm. ©2023 Journal of Northwestern Polytechnical University.
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页码：144 / 152
页数：8
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