受生物视觉启发的无人机航拍低照度图像增强算法

针对无人机航拍低照度图像存在亮度低、噪声大、细节不明显等问题, 受人类视觉系统中的双路径模型启发, 提出一种双路径模型的无人机航拍低照度图像增强算法。构建了一种基于残差单元的U-Net网络将图像分解为结构通路和细节通路; 提出了一种改进的生成对抗网络对结构通路进行增强处理, 并添加边缘增强模块来增强图像的边缘信息; 在细节通路中采取噪声抑制策略减少噪声对图像的影响; 融合2条路径的输出得到增强后的图像。实验结果表明, 新算法提高了图像的亮度和细节信息, 客观评价指标上优于其他对比算法。此外, 还验证了所提算法对低照度条件下目标检测算法的影响, 实验结果表明, 新算法能够有效提升目标检测算法的性能。

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.