Year: 2022
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Wu, Mengfei, Xue, Xucheng, Lan, Taiji, Xu, Xinwei
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4292760857