| 基于深度学习的单幅图像超分辨率重建算法综述 |
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| 引用本文： | 李佳星, 赵勇先, 王京华. 基于深度学习的单幅图像超分辨率重建算法综述. 自动化学报, 2021, 47(10): 2341−2363 doi: 10.16383/j.aas.c190859 |
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| 作者姓名： | 李佳星 赵勇先 王京华 |
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| 作者单位： | 1.长春理工大学机电工程学院 长春 130022;;2.中国科学院长春光学精密机械与物理研究所 长春 130033;;3.中国科学院大学 北京 100049;;4.长春理工大学跨尺度微纳制造教育部重点实验室 长春 130022 |
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| 基金项目： | 国防基础科研计划(JCKY2019411B001), “111”计划(D17017), 露泉创新基金(LQ-2020-01)资助 |
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| 摘 要： | 单幅图像超分辨率(Single image super-resolution, SISR)重建是计算机视觉领域上的一个重要问题, 在安防视频监控、飞机航拍以及卫星遥感等方面具有重要的研究意义和应用价值. 近年来, 深度学习在图像分类、检测、识别等诸多领域中取得了突破性进展, 也推动着图像超分辨率重建技术的发展. 本文首先介绍单幅图像超分辨率重建的常用公共图像数据集; 然后, 重点阐述基于深度学习的单幅图像超分辨率重建方向的创新与进展; 最后, 讨论了单幅图像超分辨率重建方向上存在的困难和挑战, 并对未来的发展趋势进行了思考与展望.
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| 关 键 词： | 单幅图像超分辨率 计算机视觉 深度学习 神经网络 |
| 收稿时间： | 2019-12-17 |
A Review of Single Image Super-resolution Reconstruction Algorithms Based on Deep Learning |
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| Li Jia-Xing, Zhao Yong-Xian, Wang Jing-Hua. A review of single image super-resolution reconstruction algorithms based on deep learning. Acta Automatica Sinica, 2021, 47(10): 2341−2363 doi: 10.16383/j.aas.c190859 |
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| Authors: | LI Jia-Xing ZHAO Yong-Xian WANG Jing-Hua |
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| Affiliation: | 1. College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun 130022;;2. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033;;3. University of Chinese Academy of Sciences, Beijing 100049;;4. Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022 |
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| Abstract: | Single image super-resolution (SISR) reconstruction is an important problem in the field of computer vision. It has important research significance and application value in security video surveillance, aircraft aerial photography and satellite remote sensing. In recent years, deep learning has made a breakthrough in many fields such as image classification, detection and recognition, and promoted the development of image super-resolution reconstruction technology. This paper first introduces the common public image datasets for single image super-resolution reconstruction. Then, the innovation and progress of single image super-resolution reconstruction based on deep learning are emphasized. Finally, the difficulties and challenges in the single image super-resolution reconstruction are discussed, and the future development trend is discussed. |
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| Keywords: | Single image super-resolution (SISR) computer vision deep learning neural network |
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