Low-light Enhancement Method Based on Attention Map Net
This work addresses low-light enhancement for vision tasks like detection and segmentation, but it appears incremental as it builds on existing U-Net and attention methods.
The authors tackled low-light image enhancement by proposing BrightenNet, a U-Net-based network incorporating attention mechanisms, which achieved results that preserve image details and meet human vision standards.
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.