Multi-exposure image fusion technology directly combines a sequence of images with the same scene but different exposure levels into a high-quality image with more details of scene. Aiming at the problems of poor local contrast difference and color distortion of existing algorithms, a new multi-exposure image fusion algorithm was proposed based on Retinex theoretical model. Firstly, based on Retinex theoretical model, the exposure sequence images were divided into an illumination component sequence and a reflection component sequence by using the illumination estimation algorithm, and then two sets of sequences were processed by different fusion methods. For the illumination component, the variation characteristics of global brightness of scene were guaranteed and the effects of overexposed and underexposed regions were weakened, while for the reflection component, the evaluation parameters of moderate exposure were used to better preserve the color and detail information of scene. The proposed algorithm was analyzed from both subjective and objective aspects. The experimental results show that compared with traditional algorithm based on image domain synthesis, the proposed algorithm has an average increase of 1.7% in Structural SIMilarity (SSIM) and has better effect in the processing of image color and local details.