The traditional image rain removal algorithms do not consider multiscale rain streaks and often result in loss of detailed information after the image is derained. To solve these problems, an image rain removal algorithm based on a multiflow expansion residual dense network is proposed in this study. In this algorithm, a guided filter is used for decomposing an image into a base layer and a detail layer. The mapping range can be considerably reduced by training the network with the residuals present between the rain and rainless image detail layers. Three dilated convolutions with different expansion factors are used to perform multiscale feature extraction on the detail layer to obtain more context information and extract complex and multidirectional rainline features. Further, the expanded residual dense block, which is the parameter layer of the network, is applied to enhance the propagation of features and expand the acceptance domain. The experiments conducted on synthetic and real pictures show that the proposed algorithm can effectively remove rain streaks with different densities and restore the detailed information present in an image. When compared with other algorithms, the proposed algorithm is better in terms of subjective effects and objective indicators.