Aiming at the fusion of visible and SAR images, a novel interactive fusion algorithm based on Transmembrane Differential Perception and Attention Mechanism (TDPAM Fusion) is proposed, which can effectively preserve the texture structure of visible images and detail information of SAR images. Firstly, the Cross-Modal Differential Perception Fusion module is utilized to extract complementary information from images, which can avoid the missing of true values and improve the accuracy of fusion. Secondly, the coordinate attention mechanism is employed to enhance the accuracy and efficiency of feature extraction, and improve the integration of semantic information. Finally, a feature interaction fusion algorithm is used to adaptively fuse features from SAR and visible images. A corresponding large benchmark dataset is designed for model training and testing. Experimental results demonstrate that the fusion algorithm can obtain high-quality visible images with clear SAR information. In addition, the algorithm can improve key indicators such as mutual information, spatial frequency, visual fidelity, and correlation coefficient by approximately 6.41%, 10.36%, 14.25%, and 4.74%, respectively.