  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.
