Low-light images often suffer from severe noise, low brightness, low contrast, and color deviation. While several low-light image enhancement methods have been proposed, there remains a lack of efficient methods that can simultaneously solve all of these problems. In this paper, we introduce FLW-Net, a Fast and LightWeight Network for low-light image enhancement that significantly improves processing speed and overall effect. To achieve efficient low-light image enhancement, we recognize the challenges of the lack of an absolute reference and the need for a large receptive field to obtain global contrast. Therefore, we propose an efficient global feature information extraction component and design loss functions based on relative information to overcome these challenges. Finally, we conduct comparative experiments to demonstrate the effectiveness of the proposed method, and the results confirm that FLW-Net can significantly reduce the complexity of supervised low-light image enhancement networks while improving processing effect. Code is available at https://github.com/hitzhangyu/FLW-Net

翻译：低光照图像往往存在严重噪声、低亮度、低对比度及色彩偏差等问题。尽管已有多种低光照图像增强方法被提出，但仍缺乏能同时解决上述所有问题的高效方案。本文提出FLW-Net（Fast and LightWeight Network），一种用于低光照图像增强的快速轻量级网络，能够显著提升处理速度与整体效果。为达成高效增强，我们认识到缺乏绝对参考基准以及需要大感受野获取全局对比度的挑战。为此，我们提出一种高效的全局特征信息提取组件，并基于相对信息设计损失函数以应对这些挑战。最后，通过对比实验验证了所提方法的有效性。结果表明，FLW-Net能在提升处理效果的同时，显著降低监督式低光照图像增强网络的复杂度。代码开源地址：https://github.com/hitzhangyu/FLW-Net