The existing rain removal methods cannot completely remove rain streak,and the image details are lost after rain removal.Therefore,an image rain removal method based on multi-resolution fusion dense network is proposed.The main body of the network is composed of multiple multi-resolution parallel fusion modules,which always keeps the spatial accurate high-resolution representation and receives a lot of context information from the low resolution.A multi-scale feature fusion module based on the selective convolution kernel mechanism SKNet is used to effectively aggregate features from streams with different resolutions by nonlinear method.An improved residual module is used in different resolution streams,and the convolution of multiple scales of adjacent layers is used to obtain rich rain ripple information.Dense connections are used between modules to enhance feature propagation between different modules.Experiments show that the evaluation index of the proposed method on synthetic and real rain image datasets is improved compared with other rain removal methods,and more detailed information can be retained while removing rain patterns.