In this paper, an end-to-end single image defogging algorithm is proposed which can achieve significant defogging effect under railway conditions. We present a method of fusing multi-scale feature information based on the residual network, which can extract more effective information at different scales. The method uses multi-scale convolution kernels to obtain different scale feature information, but it will increase computational cost and reduce the network depth. By increasing the bottleneck layer, we can deepen the residual network and ensure that the network can achieve better dehazing results. The proposed method can achieve better results than the state-of-the-art algorithms based on synthetic datasets and railway images.


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    Title :

    Single Image Dehazing of Railway Images via Multi-scale Residual Networks


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Liu, Baoming (editor) / Jia, Limin (editor) / Qin, Yong (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / Cao, Zhi wei (author) / Qin, Yong (author) / Xie, Zheng yu (author)

    Conference:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2019 ; Qingdao, China October 25, 2019 - October 27, 2019



    Publication date :

    2020-04-02


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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