论文标题
自我监督图像增强网络：仅使用低光图像训练
Self-supervised Image Enhancement Network: Training with Low Light Images Only
论文作者
论文摘要
本文提出了一种基于深度学习的自我监督的低光图像增强方法。受到信息熵理论和视网膜模型的启发，我们提出了一个基于最大熵的视网膜模型。使用此模型，一个非常简单的网络可以将照明和反射率分开，并且网络只能使用低光图像进行训练。我们引入了一个约束，即反射率的最大通道符合低光图像的最大通道及其熵在我们的模型中应最大，以实现自我监督的学习。我们的模型非常简单，并且不依赖任何精心设计的数据集（即使是一个低光图像也可以完成培训）。该网络只需要微小级别的培训即可增强图像。可以通过实验证明，在处理速度和效果方面，提出的方法已达到了最新的方法。
This paper proposes a self-supervised low light image enhancement method based on deep learning. Inspired by information entropy theory and Retinex model, we proposed a maximum entropy based Retinex model. With this model, a very simple network can separate the illumination and reflectance, and the network can be trained with low light images only. We introduce a constraint that the maximum channel of the reflectance conforms to the maximum channel of the low light image and its entropy should be largest in our model to achieve self-supervised learning. Our model is very simple and does not rely on any well-designed data set (even one low light image can complete the training). The network only needs minute-level training to achieve image enhancement. It can be proved through experiments that the proposed method has reached the state-of-the-art in terms of processing speed and effect.
