Abstract: Polarization image fusion improves overall image quality by combining spectral and polarization information. It is used in different fields, such as image enhancement, spatial remote sensing, target identification and military defense. In this study, based on a review of traditional fusion methods using multi-scale transform, sparse representation, pseudo-coloration, etc. we focus on the current research status of polarization image fusion methods based on deep learning. First, the research progress of polarization image fusion based on convolutional neural networks and generative adversarial networks is presented. Next, related applications in target detection, semantic segmentation, image defogging, and three-dimensional reconstruction are described. Some publicly available high-quality polarization image datasets are collated. Finally, an outlook on future research is presented.