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首页> 外文期刊>Sensing and imaging >Learning an Enhancement Convolutional Neural Network for Multi‑degraded Images
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Learning an Enhancement Convolutional Neural Network for Multi‑degraded Images

机译：学习用于多降级图像的增强卷积神经网络

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摘要

Although image enhancement methods have been widely applied in various outdoor vision systems, the existing methods still face two critical problems. On the one hand, the existing methods only consider a single degradation. However, in practical applications, image quality is usually degraded by multiple factors. The methods designed for the single degradation factor cannot achieve good performance when facing multi-degraded images. On the other hand, the imaging model-based enhancement methods which use prior knowledge or handcrafted features to perform image enhancement may bring some fitting errors. Therefore, considering multiple degradations in images, an image enhancement method is proposed in this paper. Firstly, a new image degradation model based on the multiple scattering model is proposed, which is used to characterize multiple degradations caused by haze, mixed with blur and noise. Then, an image enhancement convolutional neural network (CNN) based on ResNet is proposed to learn the implicit mapping model between low-quality and high-quality images in the pixel domain directly. The CNN network has been trained with an end-to-end learning manner. Experimental results on the synthetic dataset and real-world hazy images verify the superiority of the proposed method, while compared with the state-of-the-art methods.
机译：虽然图像增强方法已广泛应用于各种户外视觉系统，但现有方法仍然面临两个关键问题。一方面，现有方法只考虑一次劣化。然而，在实际应用中，图像质量通常因多个因素而降低。在面对多降级图像时，为单个劣化因子设计的方法无法实现良好的性能。另一方面，使用现有知识或手工特征来执行图像增强的成像模型的增强方法可能会带来一些拟合误差。因此，考虑到图像中的多重降级，本文提出了一种图像增强方法。首先，提出了一种基于多散射模型的新的图像劣化模型，用于表征由雾度引起的多种降级，与模糊和噪声混合。然后，提出了一种基于RENET的图像增强卷积神经网络（CNN），用于直接在像素域中的低质量和高质量图像之间的隐式映射模型。 CNN网络已被带有端到端学习方式培训。实验结果对合成数据集和真实世界朦胧图像验证了所提出的方法的优越性，而与最先进的方法相比。

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  • 来源
    《Sensing and imaging》 |2020年第1期|25.1-25.15|共15页
  • 作者

    Ke Wang; Li Zhuo; Jiafeng Li; Tongyao Jia; Jing Zhang;

  • 作者单位

    Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China College of Microelectronics Faculty of Information Technology Beijing University of Technology Beijing China;

    Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China College of Microelectronics Faculty of Information Technology Beijing University of Technology Beijing China;

    Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China College of Microelectronics Faculty of Information Technology Beijing University of Technology Beijing China;

    Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China College of Microelectronics Faculty of Information Technology Beijing University of Technology Beijing China;

    Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China College of Microelectronics Faculty of Information Technology Beijing University of Technology Beijing China;

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

    Image enhancement; Multi-degraded images; ResNet; Convolutional neural network;

    机译：图像增强;多降级图像;reset;卷积神经网络;

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