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首页> 外文期刊>Journal of Imaging Science and Technology >Rain Detection and Removal via Shrinkage-based Sparse Coding and Learned Rain Dictionary
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Rain Detection and Removal via Shrinkage-based Sparse Coding and Learned Rain Dictionary

机译：通过收缩的稀疏编码和学习雨析雨检测和去除

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摘要

Rain removal is essential for achieving autonomous driving because it preserves the details of objects that are useful for feature extraction and removes the rain structures that hinder feature extraction. Based on a linear superposition model in which the observed rain image is decomposed into two layers, a rain layer and a non-rain layer, conventional rain removal methods estimate these two layers alternatively from an observed single image based on prior modeling. However, the prior knowledge used for the rain structures is not always correct because various types of rain structures can be observed in the rain images, which results in inaccurate rain removal. Therefore, in this article, a novel rain removal method based on the use of a scribbled rain image set and a new shrinkage-based sparse coding model is proposed. The scribbled rain images have information about which pixels have rain structures. Thus, various types of rain structures can be modeled, owing to the abundance of rain structures in the rain image set. To detect the rain regions, two types of approaches, one based on reconstruction error comparison (REC) via a learned rain dictionary and the other based on a deep convolutional neural network (DCNN), are presented. With the rain regions, the proposed shrinkage-based sparse coding model determines how much to reduce the sparse codes of the rain dictionary and maintain the sparse codes of the non-rain dictionary for accurate rain removal. Experimental results verified that the proposed shrinkage-based sparse coding model could remove rain structures and preserve objects' details due to the REC- or DCNN-based rain detection using the scribbled rain image set. Moreover, it was confirmed that the proposed method is more effective at removing rain structures from similar objects' structures than conventional methods. (C) 2020 Society for Imaging Science and Technology.
机译：雨拆卸对于实现自动驾驶至关重要，因为它保留了对特征提取有用的物体的细节，并且除去妨碍特征提取的雨结构。基于其中观察到的雨图像被分解成两层，雨层和非雨层的线性叠加模型，传统的雨水移除方法根据先前建模的观察到的单个图像替代地估计这两层。然而，用于雨结构的现有知识并不总是正确的，因为可以在雨图像中观察各种类型的雨结构，从而导致雨水不准确。因此，在本文中，提出了一种基于使用潦草的雨量图像集和新的基于收缩的稀疏编码模型的新型雨拆卸方法。潦草的雨水图像具有关于哪些像素具有雨结构的信息。因此，由于雨图像集中的雨水结构的丰富结构，可以建模各种类型的雨结构。为了检测雨区，基于重建误差比较（REC）通过学习的雨法字典和基于深度卷积神经网络（DCNN）的另一类方法，呈现了两种方法。通过雨区，所提出的基于收缩的稀疏编码模型决定了减少雨析字典的稀疏代码的数量，并维持非雨字典的稀疏代码以进行准确雨量。实验结果证实，由于使用潦草的雨雨图像集，所提出的基于收缩的稀疏编码模型可以去除雨结构并保护物体的细节。此外，证实该方法在从类似物体结构中除去雨结构比传统方法更有效。 （c）2020年影像科技协会。

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  • 来源
    《Journal of Imaging Science and Technology》 |2020年第3期|30501.1-30501.17|共17页
  • 作者

    Son Chang-Hwan; Zhang Xiao-Ping;

  • 作者单位

    Kunsan Natl Univ Dept Software Convergence Engn 558 Daehak Ro Gunsan Si Jeollabuk Do South Korea;

    Ryerson Univ Dept Elect & Comp Engn 350 Victoria St Toronto ON M5B 2K3 Canada;

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