  Images obtained under low-light conditions will seriously affect the quality
of the images. Solving the problem of poor low-light image quality can
effectively improve the visual quality of images and better improve the
usability of computer vision. In addition, it has very important applications
in many fields. This paper proposes a DEANet based on Retinex for low-light
image enhancement. It combines the frequency information and content
information of the image into three sub-networks: decomposition network,
enhancement network and adjustment network. These three sub-networks are
respectively used for decomposition, denoising, contrast enhancement and detail
preservation, adjustment, and image generation. Our model has good robust
results for all low-light images. The model is trained on the public data set
LOL, and the experimental results show that our method is better than the
existing state-of-the-art methods in terms of vision and quality.
