FAOD-Net: A Fast AOD-Net for Dehazing Single Image

Qian Wen; Zhou Chao; Zhang Dengyin

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FAOD-Net: A Fast AOD-Net for Dehazing Single Image

机译：FAOD-Net: A Fast AOD-Net for Dehazing Single Image

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In this paper, we present an extremely computation-efficient model called FAOD-Net for dehazing single image. FAOD-Net is based on a streamlined architecture that uses depthwise separable convolutions to build lightweight deep neural networks. Moreover, the pyramid pooling module is added in FAOD-Net to aggregate the context information of different regions of the image, thereby improving the ability of the network model to obtain the global information of the foggy image. To get the best FAOD-Net, we use the RESIDE training set to train our proposed model. In addition, we have carried out extensive experiments on the RESIDE test set. We use full-reference and no-reference image quality evaluation indicators to measure the effect of dehazing. Experimental results show that the proposed algorithm has satisfactory results in terms of defogging quality and speed.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2020年第5期|4945214.1-4945214.11|共11页
作者
Qian Wen; Zhou Chao; Zhang Dengyin;
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Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China|Nanjing Univ Posts & Telecommun, Jiangsu Key Lab Broadband Wireless Commun & Inter, Nanjing 210003, Peoples R China;

Nanjing Univ Posts & Telecommun, Jiangsu Key Lab Broadband Wireless Commun & Inter, Nanjing 210003, Peoples R China|Nanjing Univ Posts & Telecommun, Sch Internet ings, Nanjing 210003, Peoples R China;

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FAOD-Net: A Fast AOD-Net for Dehazing Single Image

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