受生物视觉启发的无人机航拍低照度图像增强算法

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.