Retinex model is an effective tool for low-light image enhancement. It
assumes that observed images can be decomposed into the reflectance and
illumination. Most existing Retinex-based methods have carefully designed
hand-crafted constraints and parameters for this highly ill-posed
decomposition, which may be limited by model capacity when applied in various
scenes. In this paper, we collect a LOw-Light dataset (LOL) containing
low/normal-light image pairs and propose a deep Retinex-Net learned on this
dataset, including a Decom-Net for decomposition and an Enhance-Net for
illumination adjustment. In the training process for Decom-Net, there is no
ground truth of decomposed reflectance and illumination. The network is learned
with only key constraints including the consistent reflectance shared by paired
low/normal-light images, and the smoothness of illumination. Based on the
decomposition, subsequent lightness enhancement is conducted on illumination by
an enhancement network called Enhance-Net, and for joint denoising there is a
denoising operation on reflectance. The Retinex-Net is end-to-end trainable, so
that the learned decomposition is by nature good for lightness adjustment.
Extensive experiments demonstrate that our method not only achieves visually
pleasing quality for low-light enhancement but also provides a good
representation of image decomposition.

该论文提出了一种基于深度学习的 Retinex 模型，可以更好地应对不同环境下光照不足的图像增强问题，通过红外辅助拍摄低光图像构建了一个 LOL（Low-Light）数据集，并使用其中的图像进行训练，实验结果表明该方法在增强低光图像质量方面具有很好的效果。