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. The code is available at \url{https://github.com/hitzhangyu/FLW-Net}.

翻译：图像增强是缓解低光照图像中严重噪声、低亮度、低对比度和颜色偏差等问题的常用技术。然而，为低光照图像增强任务提供理想的高光照图像作为参考是不可能的，这使得该任务的学习过程比其他图像处理任务更加困难。因此，尽管已有多种低光照图像增强方法被提出，但大多数方法要么过于复杂，要么无法充分解决低光照图像中的所有问题。本文为简化低光照图像增强的学习过程，提出了FLW-Net（快速轻量化网络）和两种相对损失函数。具体而言，我们首先认识到该任务中需要大感受野以获取全局对比度以及缺乏绝对参考这两大挑战，这些挑战限制了网络结构的简化。随后，我们提出了一种高效的全局特征信息提取组件以及两种基于相对信息的损失函数来应对这些挑战。最后，我们通过对比实验验证了所提方法的有效性，结果表明该方法能显著降低监督式低光照图像增强网络的复杂度，同时提升处理效果。代码已开源在 \url{https://github.com/hitzhangyu/FLW-Net}。