  This paper proposes a self-supervised low light image enhancement method
based on deep learning, which can improve the image contrast and reduce noise
at the same time to avoid the blur caused by pre-/post-denoising. The method
contains two deep sub-networks, an Image Contrast Enhancement Network (ICE-Net)
and a Re-Enhancement and Denoising Network (RED-Net). The ICE-Net takes the low
light image as input and produces a contrast enhanced image. The RED-Net takes
the result of ICE-Net and the low light image as input, and can re-enhance the
low light image and denoise at the same time. Both of the networks can be
trained with low light images only, which is achieved by a Maximum Entropy
based Retinex (ME-Retinex) model and an assumption that noises are
independently distributed. In the ME-Retinex model, a new constraint on the
reflectance image is introduced that the maximum channel of the reflectance
image conforms to the maximum channel of the low light image and its entropy
should be the largest, which converts the decomposition of reflectance and
illumination in Retinex model to a non-ill-conditioned problem and allows the
ICE-Net to be trained with a self-supervised way. The loss functions of RED-Net
are carefully formulated to separate the noises and details during training,
and they are based on the idea that, if noises are independently distributed,
after the processing of smoothing filters (\eg mean filter), the gradient of
the noise part should be smaller than the gradient of the detail part. It can
be proved qualitatively and quantitatively through experiments that the
proposed method is efficient.
