When imaging equipment takes images on rainy days, the image quality will be seriously degraded due to the existence of rain fog and rain stripes, which will greatly affect the subsequent image processing performance. Therefore, the research on image rain removal algorithms has attracted wide attention, and the rain removal algorithm for a single image is facing great challenges because it is not supported by prior knowledge. In recent years, deep learning has been applied to the research on image rain removal algorithms because of its high feature representation ability. In this study, based on wavelet transform, an algorithm combining deep learning with morphological processing of digital images is adopted to remove rain from a single image, which has the advantages of a few training parameters, short training time, and good rain removal effects. Firstly, the image containing rain is decomposed into a low-frequency component, horizontal high-frequency component, vertical high-frequency component, and diagonal high-frequency component by wavelet transform. Then, the four components are constructed into deep learning neural networks respectively, and morphological processing such as image expansion and corrosion is added to the neural network architecture according to the rain features to remove rain, which greatly simplifies the model architecture and can achieve good results.