Simplifying Low-Light Image Enhancement Networks with Relative Loss Functions

分类： 计算机科学 >> 计算机应用技术 提交时间： 2023-10-08

Yu Zhang Xiaoguang Di Junde Wu Yue Wang Yong Li Rao Fu Yanwu Xu Guohui Yang Chunhui Wang

摘要： Image enhancement is a common technique used to mitigate issues such as severe noise, low brightness, low contrast, and color deviation in low-light images. However, providing an optimal high-light image as a reference for low-light image enhancement tasks is impossible, which makes the learning process more difficult than other image processing tasks. As a result, although several low-light image enhancement methods have been proposed, most of them are either too complex or insufficient in addressing all the issues in low-light images. In this paper, to make the learning easier in low-light image enhancement, we introduce FLW-Net (Fast and LightWeight Network) and two relative loss functions. Specifically, we first recognize the challenges of the need for a large receptive field to obtain global contrast and the lack of an absolute reference, which limits the simplification of network structures in this task. Then, we propose an efficient global feature information extraction component and two loss functions based on relative information to overcome these challenges. Finally, we conducted comparative experiments to demonstrate the effectiveness of the proposed method, and the results confirm that the proposed method can significantly reduce the complexity of supervised low-light image enhancement networks while improving processing effect.

同行评议状态:待评议

 点击量 3511  下载量 179  评论