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 state-of-the-art methods. We also test our method qualitatively on the real-world 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 image-based high-level 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.