Analysis and Synthesis of Image Dehazing Using Deep Learning Algorithm
Keywords:
Convolution Neural Network, Image-Dehazing, Dark Channel Prior, Guided Filter, Atrous Spatial Pyramid PoolingAbstract
Artificial intelligence technology is revolutionizing the automation, dependability, and robustness of industrial sectors while also improving overall quality and production. Visual sensor networks are used by most industrial and surveillance industries to monitor their surroundings by capturing numerous pictures of it. Polluted suspended atmospheric particles, on the other hand, degrade the entire monitoring system and the image quality during severe weather. This paper provides a lightweight convolutional neural network that is computationally effective and is utilized for picture reconstruction to address these problems. The proposed module adjusted the atmospheric effects models to jointly evaluate the “transmission map” and the “atmospheric light”, unlike current learning-based systems that assess the “transmission map” and the atmospheric light separately. An extension to the Atrous Spatial Pyramid Pooling (ASPP) approach is used to construct a context vector in a bottleneck that is made up of multi-scale context information to reduce colour distortion in the dehaze image. The quantitative and qualitative examination of many photographs from the NH-Haze dataset supports the suggestion's superiority over existing image dehazing methods.
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