论文标题
基于注意图网的低光增强方法
Low-light Enhancement Method Based on Attention Map Net
论文作者
论文摘要
低光图像增强是某些复杂视觉任务的至关重要的预处理任务。目标检测，图像分割和图像识别结果都受图像增强的影响直接影响。但是，当前使用的大多数图像增强技术不会产生令人满意的结果，并且这些增强的网络具有相对较弱的鲁棒性。我们建议使用U-NET作为其主要结构的改进的网络，并将许多不同的注意机制作为解决此问题的解决方案。在特定的应用程序中，我们将网络用作生成器和LSGAN作为培训框架，以获得更好的增强结果。我们证明了本文随后的实验中提出的网络Brightennet的有效性。它产生的结果既可以保留图像细节，又符合人类视力标准。
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.
