  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.

Computer Vision and Pattern Recognition;Image and Video Processing