Abstract
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 image signal processing literature. However, current deep learning-based solutions struggle with efficiency and robustness in real-world scenarios (e.g. scenes with noise, saturated pixels, bad illumination). We propose a lightweight neural network that combines image processing in the frequency and spatial domains. Our method, FLOL+, is one of the fastest models for this task, achieving state-of-the-art results on popular real scenes datasets such as LOL and LSRW. Moreover, we are able to process 1080p images under 12ms. Code and models at this https URL
Abstract (translated)
低光图像增强（LLIE）是计算摄影和成像中的一个关键任务。夜间或在光线较暗的环境中拍摄的照片增强问题已经在图像信号处理文献中得到了充分的研究。然而，当前基于深度学习的方法在实际场景中的效率和鲁棒性方面仍然存在问题（例如噪声、饱和像素以及不良照明条件）。我们提出了一种结合频域与空域图像处理的轻量级神经网络。我们的方法FLOL+是执行此任务最快的模型之一，在流行的现实场景数据集（如LOL和LSRW）上实现了最先进的性能。此外，我们可以以不到12毫秒的时间处理1080p分辨率的图片。代码与模型可在该网址获得：[请在此处插入实际URL]。
URL
https://arxiv.org/abs/2501.09718
