Low-light image enhancement is a crucial preprocessing task for some complex vision tasks. Target detection, image segmentation, and image recognition outcomes are all directly impacted by the impact of image enhancement. However, the majority of the currently used image enhancement techniques do not produce satisfactory outcomes, and these enhanced networks have relatively weak robustness. We suggest an improved network called BrightenNet that uses U-Net as its primary structure and incorporates a number of different attention mechanisms as a solution to this issue. In a specific application, we employ the network as the generator and LSGAN as the training framework to achieve better enhancement results. We demonstrate the validity of the proposed network BrightenNet in the experiments that follow in this paper. The results it produced can both preserve image details and conform to human vision standards.

翻译：低光照图像增强是一些复杂视觉任务中的关键预处理步骤。图像增强的效果直接影响目标检测、图像分割和图像识别的结果。然而，当前使用的大多数图像增强方法未能产生令人满意的结果，且这些增强网络的鲁棒性相对较弱。针对这一问题，我们提出了一种名为BrightenNet的改进网络，该网络以U-Net作为主要结构，并融合了多种不同的注意力机制。在具体应用中，我们将该网络作为生成器，并以LSGAN作为训练框架，以获得更好的增强效果。在本文后续的实验中，我们验证了所提出的BrightenNet网络的有效性。其生成的结果既能保留图像细节，又能符合人类视觉标准。