  In recent years, deep learning based methods have made significant progress
in rain-removing. However, the existing methods usually do not have good
generalization ability, which leads to the fact that almost all of existing
methods have a satisfied performance on removing a specific type of rain
streaks, but may have a relatively poor performance on other types of rain
streaks. In this paper, aiming at removing multiple types of rain streaks from
single images, we propose a novel deraining framework (GRASPP-GAN), which has
better generalization capacity. Specifically, a modified ResNet-18 which
extracts the deep features of rainy images and a revised ASPP structure which
adapts to the various shapes and sizes of rain streaks are composed together to
form the backbone of our deraining network. Taking the more prominent
characteristics of rain streaks in the gradient domain into consideration, a
gradient loss is introduced to help to supervise our deraining training
process, for which, a Sobel convolution layer is built to extract the gradient
information flexibly. To further boost the performance, an adversarial learning
scheme is employed for the first time to train the proposed network. Extensive
experiments on both real-world and synthetic datasets demonstrate that our
method outperforms the state-of-the-art deraining methods quantitatively and
qualitatively. In addition, without any modifications, our proposed framework
also achieves good visual performance on dehazing.
