Due to the different physical imaging models, most haze or rain removal methods for daytime images are not suitable for nighttime images. Fog effect produced by the accumu-lation of rain also brings great challenges to the restoration of lowlight nighttime images. To deal well with the multi-ple noise interference in this complex situation, we propose a saliency-guided dynamic restoration network (SDRNet) that can remove rain and haze in nighttime scenes. First, a saliency-guided detail enhancement preprocessing method is designed to get images with clearer details as the auxilia-ry input. Second, following a rain removal network (RRN), we design an all-in-one nighttime dehazing network (ANDN) to estimate the spatially variable ambient light and transmission comprehensively by deforming the nighttime haze image model. Finally, an attention-based enhancement network (AEN) with dynamic fusion attention module is proposed to enhance the lowlight background image. Experimental results indicate that SDRNet can ob-tain clearer images with less fog and distortion compared with other methods.