Images captured in weak illumination conditions could seriously degrade the
image quality. Solving a series of degradation of low-light images can
effectively improve the visual quality of images and the performance of
high-level visual tasks. In this study, a novel Retinex-based Real-low to
Real-normal Network (R2RNet) is proposed for low-light image enhancement, which
includes three subnets: a Decom-Net, a Denoise-Net, and a Relight-Net. These
three subnets are used for decomposing, denoising, contrast enhancement and
detail preservation, respectively. Our R2RNet not only uses the spatial
information of the image to improve the contrast but also uses the frequency
information to preserve the details. Therefore, our model acheived more robust
results for all degraded images. Unlike most previous methods that were trained
on synthetic images, we collected the first Large-Scale Real-World paired
low/normal-light images dataset (LSRW dataset) to satisfy the training
requirements and make our model have better generalization performance in
real-world scenes. Extensive experiments on publicly available datasets
demonstrated that our method outperforms the existing state-of-the-art methods
both quantitatively and visually. In addition, our results showed that the
performance of the high-level visual task (i.e. face detection) can be
effectively improved by using the enhanced results obtained by our method in
low-light conditions. Our codes and the LSRW dataset are available at:
this https URL

本文提出了一种新的基于 Retinex 的深度学习网络，用于低光条件下的图像增强。该网络包含三个子网络：Decom-Net，Denoise-Net 和 Relight-Net。我们的模型使用了来自图像的空间信息和频率信息，可以在低光条件下取得更鲁棒的结果，并且在高级视觉任务方面具有显著的改善表现。我们还提出了一个大规模真实世界数据集（LSRW 数据集），用于训练模型，展示模型在真实世界场景中具有更好的泛化能力。经过广泛的实验，我们的方法在定量和视觉方面都表现出优于现有状态 - of-the-art 的结果。