Low-light Image Enhancement Algorithm Combining Multi-Scale Deep Learning Networks and Retinex Theory

‍ ‍This paper proposes a multi-scale weighted feature low light image enhancement algorithm based on Retinex theory and attention mechanism， aiming at the lack of reference images for low light enhancement tasks and the problems of color distortion， texture loss， detail blur， and difficulty in obtaining truth images in existing algorithms. In this algorithm， the feature extraction module based on UNET architecture performs multi-scale feature extraction on low illumination images to generate high-dimensional multi-scale feature maps； the attention mechanism module is established to highlight the feature information of different scales beneficial to the enhanced image and obtain a weighted high-dimensional feature map； finally， in the reflection estimation module， the network model is established by using Retinex theory， and the final enhanced image is generated by the high-dimensional feature map. An end-to-end network architecture is designed， and a group of self regular loss functions is used to constrain the network model， so as to get rid of the constraints of reference images and realize unsupervised learning. The final experimental results show that this algorithm can enhance the contrast and clarity of the image while maintaining high image detail and texture， and has good visual effect. Our algorithm can effectively enhance the low illumination image and greatly improve its visual quality.