SDRNet: Saliency-Guided Dynamic Restoration Network for Rain and Haze Removal in Nighttime Images

Abstract

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 accumulation of rain also brings great challenges to the restoration of low-light nighttime images. To deal well with the multiple 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 auxiliary 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 obtain clearer images with less fog and distortion compared with other methods.