A CoarsetoFine Multistream Hybrid Deraining Network for Single Image Deraining Single image deraining task is still a very challenging task due to its illposed nature in reality. Recently, researchers have tried to fix this issue by training the CNNbased endtoend models, but they still cannot extract the negative rain streaks from rainy images precisely, which usually leads to an over derained or under derained result. To handle this issue, this paper proposes a new coarsetofine single image deraining framework termed Multistream Hybrid Deraining Network (shortly, MHDerainNet). To obtain the negative rain streaks during training process more accurately, we present a new module named dual path residual dense block, i.e., Residual path and Dense path. The Residual path is used to reuse common features from the previous layers while the Dense path can explore new features. In addition, to concatenate different scaled features, we also apply the idea of multistream with shortcuts between cascaded dual path residual dense block based streams. To obtain more distinct derained images, we combine the SSIM loss and perceptual loss to preserve the perpixel similarity as well as preserving the global structures so that the deraining result is more accurate. Extensive experiments on both synthetic and real rainy images demonstrate that our MHDerainNet can deliver significant improvements over several recent stateoftheart methods.

A Convolutional Network for Joint Deraining and Dehazing from A Single Image for Autonomous Driving in Rain In this paper, we focus on a rain removal task from a single image of the urban street scene for autonomous driving in rain. We develop a Convolutional Neural Network which takes a rainy image as input, and directly recovers a clean image in the presence of rain streaks, atmospheric veiling effect (haze, fog, mist) caused by distant rain streak accumulation. We propose a synthetic dataset containing images of urban street scenes with different rain intensities, orientations and haziness levels for training and evaluation. We evaluate our method quantitatively and qualitatively on the synthetic data. Experiments show that our model outperforms stateoftheart methods. We also test our method qualitatively on the realworld data. Our model is fast and it takes 0.05s for an image of 1024 times 512. Our model can be seamlessly integrated with existing imagebased highlevel perception algorithms for autonomous driving in rain. Experiment results show that our deraining method improves semantic segmentation and object detection largely for autonomous driving in rain.