High dynamic range imaging images are images that truly represent the high dynamic range brightness of natural scenes, and can reflect more information about natural scenes. Multi-exposure fusion has become one of the important means to reconstruct high dynamic range images due to its advantages of no need to improve hardware and simple algorithm process, and has been widely used in mobile phone cameras, industrial cameras, and other fields. In this paper, the multi-exposure image fusion methods for static scenes and dynamic scenes were classified and summarized according to the fusion level and motion pixel processing methods, and the methods based on deep learning were analyzed and summarized separately. Secondly, the relevant datasets and performance evaluation indicators of multi-exposure image fusion were reviewed, and the performance evaluation indicators used in the fusion method were summarized. Finally, the issues worthy of attention in multi-exposure image fusion research were prospected, and ideas for follow-up related research were provided.