The enhancement of catenary image with low visibility based on multifeature fusion network in railway industry Abstract   In the Industrial Internet of Things (IIoT), the security and efficiency are indispensable. For the railway industry, the video from inspection vehicle would be influenced by various factors with low visibility and hard for high level vision task, such as the fault diagnosis of catenary system. In this paper, we propose a method based on the multifeature fusion network to improve the quality and visual effect of the catenary images. The transmission map is learned from the multiscale and multifeature fusion network, which would learn coarse and fine details and combine the latent features. In the catenary image, the sky and nonsky regions are segmented through multiple accommodative thresholds to estimate the atmospheric light value. With the refinement of transmission map, the restored catenary image is obtained through the atmospheric scattering model. In the experimental results, it can be seen that the proposed method can improve the clarity of catenary image in haze. The quantitative evaluation shows that it has better visual effect compared with the other traditional methods.

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.