Multi-scale Feature Recovery of Low-Light Enhancement Algorithm Based on U-net Network and Perceptual Loss

How to handle low-light images is a challenging problem in the field of image processing. A mature low-light enhancement technique not only benefits human visual perception, but also can lay a good foundation for subsequent advanced tasks such as target detection and image classification. To balance the visual effect of an image and its contribution to subsequent tasks, we propose an innovative multi-scale feature recovery low-light enhancement model combining U-net network and perceptual loss. It first passes the image through a denoising module to reduce the uncertainty of the subsequent enhancement process, and then trains a tandem “end-to-end” network with a synthetic image training dataset to complete the entire enhancement process. The unique U-shaped structure with multiple loss function constraints allows the algorithm to better recover feature information at different scales of the image, resulting in a more distinctive resultant image with normal illumination. Through extensive experiments on synthetic low-light images and real low-light images, the analysis results show that the algorithm can not only make the low-light image enhancement results with more natural colors, which can bring a pleasant visual experience to the observer and satisfy the basic low-light enhancement needs, but also recover as many features and image details as possible, which is beneficial to the subsequent image vision tasks.