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.
@article{arxiv.2208.09330,
title = {Low-light Enhancement Method Based on Attention Map Net},
author = {Mengfei Wu and Xucheng Xue and Taiji Lan and Xinwei Xu},
journal= {arXiv preprint arXiv:2208.09330},
year = {2023}
}
Comments
This paper contains some errors in the analysis presented in the introduction section, such as misunderstanding some of the improved methods in comparison to traditional methods like histogram equalization. These errors have impacted the quality and reliability of my research, and could potentially mislead readers and colleagues