Recent progress in low-light image enhancement algorithms based on deep learning

In low light environments, the collected images have characteristics such as low contrast, low signal-to-noise ratio, and loss of details, leading to an overall decline in image quality. Low light image enhancement aims to restore images with complete details and has gradually become a research hotspot in computer image processing. With the large-scale increase in data volume in recent years, deep learning based methods have gradually become mainstream. This article provides a detailed classification and analysis of low light image enhancement methods based on deep learning, sorts out various networks used, introduces the basic principles and steps of various algorithms, and introduces the existing low light image enhancement datasets and evaluation methods. Finally, a summary of the content was made, pointing out the difficulties in current research and providing prospects for future research directions.