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Single image deraining using deep convolutional networks

机译:使用深度卷积网络消除单幅图像

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摘要

A deep learning-based single image deraining algorithm is proposed in this work. Instead of modeling a rain layer as a linear function between the rain image and its clear version as previous works do, we directly formulate the clear image as the result of a non-linear mapping of thrain image. We construct a coarse deraining convolutional network and a refinement convolutional network to learn this non-linear mapping function. The coarse deraining network is trained to detect the rain streaks with different directions, and restore a raw derained result. The refinement network aims at refining the result according to the raw derained image and the original rain image. By combining the two networks, we are able to well-restore the rain-free image. Experimental results demonstrate that the proposed deraining method can produce high-quality clear images from both synthetic and real-world rain images, outperforming the state-of-the-art methods qualitatively and quantitatively.
机译:在这项工作中,提出了一种基于深度学习的单图像去除算法。我们没有像以前的工作那样将雨层建模为雨图与其清晰版本之间的线性函数,而是直接将清晰图像公式化为Thrain图像的非线性映射的结果。我们构造了一个粗化的排水卷积网络和一个精炼卷积网络来学习这种非线性映射函数。训练粗排水网络可以检测不同方向的雨水条纹,并恢复原始的排水结果。细化网络旨在根据原始的排水图像和原始降雨图像细化结果。通过结合两个网络,我们可以很好地恢复无雨图像。实验结果表明,所提出的排水方法可以从合成和真实的降雨图像中生成高质量的清晰图像,在质量和数量上均优于最新方法。

著录项

  • 来源
    《Multimedia Tools and Applications》 |2018年第19期|25905-25918|共14页
  • 作者单位

    South China Agr Univ, Coll Math & Informat, Guangzhou, Guangdong, Peoples R China;

    South China Agr Univ, Coll Math & Informat, Guangzhou, Guangdong, Peoples R China;

    Guangdong Univ Technol, Sch Comp Sci & Technol, Guangzhou, Guangdong, Peoples R China;

    South China Agr Univ, Coll Math & Informat, Guangzhou, Guangdong, Peoples R China;

    South China Agr Univ, Coll Math & Informat, Guangzhou, Guangdong, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Image deraining; image restoration; image enhancement; deep learning;

    机译:图像排水;图像恢复;图像增强;深度学习;

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