Abstract: Low-light image enhancement is an important problem in the field of image processing. The rapid development of deep learning technology provides a new solution for low-light image enhancement and has broad application prospects. First, the current research status and challenges in the field of low-light image enhancement are comprehensively analyzed, and traditional methods and their advantages and disadvantages are introduced. Second, deep learning-based low-light image enhancement algorithms are classified into five categories according to their different learning strategies, and the principles, network structures, and problem-solving capabilities of these algorithms are explained in detail. Third, representative deep learning-based image enhancement algorithms from the last six years are compared and analyzed in chronological order. Fourth, the current mainstream datasets and evaluation indexes are summarized, and the deep learning algorithms are tested and evaluated in terms of perceived similarity and algorithm performance. Finally, directions for improvement and future research in the field of low-light image enhancement are discussed and suggested.