  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:
https://github.com/abcdef2000/R2RNet.
