图像超分辨重建算法综述
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北京航空航天大学宇航学院图像处理中心,北京,100191

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国家重点研发计划 2017YFC1405605┫资助项目 ; 国家自然科学基金 61671037┫资助项目 ; 北京市自然科学基金 4192034┫资助项目 国家重点研发计划(2017YFC1405605)资助项目;国家自然科学基金(61671037)资助项目;北京市自然科学基金(4192034)资助项目。


Review of Image Super-Resolution Reconstruction
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Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China

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    摘要:

    图像超分辨重建是一种提升图像分辨率的图像处理技术,而超分辨问题是一个难解的欠定问题,近些年来研究人员主要采用基于学习的方法,从大量数据中学习图像先验信息,以实现对解空间的约束。本文介绍了近20年来主流的图像超分辨重建算法,主要分为基于传统特征的方法和基于深度学习的方法。对于传统的超分辨重建算法,阐述了基于邻域嵌入的方法、基于稀疏表示的方法以及基于局部线性回归的方法。对于基于深度学习的超分辨重建算法,详细总结了网络模型结构设计、上采样方式、损失函数形式以及复杂条件下的算法设计4个方面。此外,本文简要分析了超分辨重建技术在视频超分辨、遥感图像超分辨以及在视觉高层任务方面的应用。最后,本文展望了图像超分辨重建技术的未来发展方向。

    Abstract:

    Image super-resolution reconstruction is an image processing technology, which recovers high-resolution images from low-resolution images. While, the super-resolution problem is under-determined. In recent years, researchers have proposed learning-based methods to learn image prior information from a large amount of data, in order to constrain the super-resolution solution space. This paper introduces the mainstream image super-resolution reconstruction algorithms in the past two decades, which are divided into two categories: traditional features based methods and deep learning based methods. For the traditional super-resolution reconstruction algorithms, this paper mainly presents the methods based on neighborhood embedding, the methods based on sparse representation, and the methods based on local linear regression. For the deep learning based methods, the super-resolution model design, the up-sampling method and the loss function form are provided. In addition, this paper introduces the application of super-resolution reconstruction technology in video super-resolution, remote-sensing image super-resolution, and high-level vision tasks. Finally, the future development directions of image super-resolution reconstruction technology are provided.

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史振威,雷森.图像超分辨重建算法综述[J].数据采集与处理,2020,35(1):1-20

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  • 收稿日期:2019-11-30
  • 最后修改日期:2019-12-25
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  • 在线发布日期: 2020-03-13