RealTime Video Dehazing for Industrial Image Processing In todayxe2x80x99s industries, automation, reliability, robustness and accuracy are pivotal problem to cut costs and increase productivity and quality. Visual sensor networks are vital control and monitoring tools for continues, online imaging and real time image processing in production and plant process. Most of the industrial videos are captured in hazy weather and usually degraded by suspended particles of atmosphere, such as smoke, fog, rain, and snow, which limits the visual quality of image. This hinders the ability of artificial intelligent driven systems to achieve automation, reliability and accuracy. Recovery of the clear visuals from the input hazy videos is challenging problem. Instead of relying on explicitly estimating the key component of atmospheric scattering model, we present endtoend CNN model, which directly recovers the clear images from hazy images. This endtoend architecture makes it an ideal preprocessing tool into other deep models for increasing the efficiency of various computer vision tasks in real time systems, such as RetinaNet for object detection, ResNet for object recognition. Experimental results demonstrate the effectiveness and robustness of proposed framework by outperforming the statoftheart approaches in terms of time complexity and visual quality.

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.