Multi-Scale Weighted Fusion Attentive Generative Adversarial Network for Single Image De-Raining

Abstract

With the rapid development of outdoor vision system, removing rain streaks from a single image has attracted considerable attention as rain streaks can affect the quality of the image taken in rainy days, and interrupt the key information, which will greatly reduce the use value of the image, thus affecting the performance of traffic, safety monitoring and other facilities. Although the deep learning methods have achieved satisfying performance in single image de-raining, there are still two problems: First, the rain streaks contained in one dataset we can use are limited, and in the case of real rainy days, the rain streak density is diverse, it is impossible to accurately classify them. Therefore, 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. Secondly, the results of single image after rain removal model often appear the phenomenon of variegated spots, image contrast saturation change and even unsmooth rain streak after rain removal. We use a three-way multi-scale weighted fusion module to enhance the feature extraction, and then generate an attention map through the improved spatial attentive module to accurately locate the location of the rain streaks. After the combination of the two, we will obtain the foreground information of the rain streaks. Through the characteristic of mutual game in the training mechanism of GAN, we can enhance the rain streak location recognition and effectively remove the rain at the same time. Through the training mechanism of the GAN network game, we can enhance the rain line location recognition and effectively remove the rain at the same time. Experiments show that our network achieves superior performance, it has high generalization for different rain streak density, and ensures that the contrast and saturation of the image are not changed.