Dilated Residual Spatial Attentive Generative Adversarial Network for Single Image De-raining

Abstract

This paper propose a dilated residual spatial attentive generative adversarial network for single image de-raining. The improved spatial attentive mechanism is combined with the dilated convolution residual module to optimize the Condition Generative Adversarial Network(CGAN) structure, to solve two problems still exist in the task of removing rain from a single image: First, the rain streaks contained in the dataset we can use are limited, and in the case of real rainy days, the rain streak density is diverse, it is impossible to simulate them completely and accurately. Then, the existing rain removal models cannot remove rain streaks properly for images with different rain streak density which attend to over or under rain removal. In our methods, Firstly, the dilated convolution module is used to enhance the feature extraction of rain streaks, and then an attention map is generated by the spatial attention mechanism module to accurately locate the position of rain streaks, and the rain streaks are extracted as the foreground information by combining the two parts of the network to guide the subsequent rain removal operation; Then the rain streaks is removed through the Contextual auto-encoder, combining with PatchGAN discriminator. The experimental results on two synthetic dataset and real-world rain image show that the network model proposed in this paper has high generalization ability under different rainfall conditions. it improves the rain removal effect of the model on the most realistic public dataset, and improves the recovery ability of image details while effectively removing rain on other public datasets, which verifies the effectiveness of the model in improving the generalization ability and improving the image quality after rain removal. At the same time, the effect of rain removal is better than other methods on the real rain images, which proves that the method proposed in this paper has high practical application value.