Low-Light Image Enhancement (LLIE) is a key task in computational photography and imaging. The problem of enhancing images captured during night or in dark environments has been well-studied in the computer vision literature. However, current deep learning-based solutions struggle with efficiency and robustness for real-world scenarios (e.g., scenes with noise, saturated pixels). We propose a lightweight neural network that combines image processing in the frequency and spatial domains. Our baseline method, FLOL, is one of the fastest models for this task, achieving results comparable to the state-of-the-art on popular real-world benchmarks such as LOLv2, LSRW, MIT-5K and UHD-LL. Moreover, we are able to process 1080p images in real-time under 12ms. Code and models at https://github.com/cidautai/FLOL

翻译：低光照图像增强（LLIE）是计算摄影与成像领域的关键任务。在计算机视觉文献中，针对夜间或暗光环境下捕获图像的增强问题已有深入研究。然而，当前基于深度学习的解决方案在真实场景（例如存在噪声、饱和像素的场景）中面临效率与鲁棒性不足的挑战。本文提出一种在频域与空间域协同进行图像处理的轻量级神经网络。我们的基准方法FLOL是该任务中最快速的模型之一，在LOLv2、LSRW、MIT-5K和UHD-LL等主流真实场景基准测试中取得了与当前最优方法相当的结果。此外，该方法能够在12毫秒内实时处理1080p分辨率图像。代码与模型发布于https://github.com/cidautai/FLOL