图像超分辨率重建研究综述
A Survey of Image Super-Resolution Reconstruction
查看参考文献60篇
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文摘
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图像超分辨率重建(Super-resolution Reconstruction,SR)是由一张或多张低分辨率图像得到高分辨率图像的过程.近年来,SR技术不断发展,在许多领域被广泛应用.本文在回顾SR技术发展历史的基础上,全面综述了SR技术在各个时期的代表性方法,重点介绍了基于深度学习的图像超分辨率工作.我们从模型类型、网络结构、信息传递方式等方面对各种算法进行了详细评述,并对比了其优缺点.最后探讨了图像超分辨率技术未来的发展方向. |
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其他语种文摘
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Image super-resolution reconstruction(SR)aims to obtain high-resolution images from one or more low-resolution images.Recently,SR has been developing and widely applied in different fields.This survey retrospects the history of SR technique and provides a comprehensive overview of representative SR methods,with an emphasis on recent deep learning-based approaches.We elaborate the details of various deep learning-based SR methods,including their strengths and weakness,in terms of the deep learning model,architecture,and message pass.Finally,we discuss the possible research directions on SR technique. |
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来源
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电子学报
,2020,48(7):1407-1420 【核心库】
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DOI
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10.3969/j.issn.0372-2112.2020.07.022
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关键词
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图像超分辨率
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深度学习
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图像处理
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方法综述
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地址
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1.
东南大学自动化学院, 江苏, 南京, 210096
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中国传媒大学信息与通信工程学院, 北京, 100024
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语种
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中文 |
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文献类型
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综述型 |
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ISSN
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0372-2112 |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金
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江苏省自然科学基金
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流程工业综合自动化国家重点实验室基金
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中国传媒大学优秀博导组项目
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中国传媒大学重大攻关培育项目-媒介事件中的AI新闻生产系统与关键技术
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文献收藏号
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CSCD:6770211
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参考文献 共
60
共3页
|
|
1.
Tsai R Y. Multiframe image restoration and registration.
Advance Computer Visual and Image Processing,1984,1:317-339
|
CSCD被引
5
次
|
|
|
|
|
2.
吴秀秀. 基于配准的肺4D-CT图像超分辨率重建.
电子学报,2015,43(2):383-386
|
CSCD被引
5
次
|
|
|
|
|
3.
Jia Z. Fast face hallucination with sparse representation for video surveillance.
First Asian Conference on Pattern Recognition (ACPR),2011:179-183
|
CSCD被引
1
次
|
|
|
|
|
4.
Hu M G. Super-resolution reconstruction of remote sensing images using multifractal analysis.
Sensors,2009,9(11):8669-8683
|
CSCD被引
2
次
|
|
|
|
|
5.
Harris J L. Difiraction and resolving power.
Journal of the Optical Society of America,1964,54(7):931-936
|
CSCD被引
87
次
|
|
|
|
|
6.
Goodman J W.
Introduction to Fourier Optics,1968
|
CSCD被引
115
次
|
|
|
|
|
7.
Borman S.
Spatial Resolution Enhancement of Low-resolution Image Sequences:A Comprehensive Review with Directions for Future Research,Technical Report,1998
|
CSCD被引
1
次
|
|
|
|
|
8.
Park S C. Super-resolution image reconstruction:a technical overview.
Signal Processing Magazine IEEE,2003,20(3):21-36
|
CSCD被引
240
次
|
|
|
|
|
9.
苏衡. 超分辨率图像重建方法综述.
自动化学报,2013,39(8):202-1213
|
CSCD被引
1
次
|
|
|
|
|
10.
Huang D. A short survey of image super resolution algorithms.
Journal of Computer Science Technology Updates,2015,2(2):19-29
|
CSCD被引
2
次
|
|
|
|
|
11.
Hayat K. Super-resolution via deep learning.
arXiv:1706.09077,2017
|
CSCD被引
2
次
|
|
|
|
|
12.
Sun X. Review on deep learning based image super-resolution restoration algorithms.
Acta Automatica Sinica,2017,43(5):697-709
|
CSCD被引
5
次
|
|
|
|
|
13.
Yang W. Deep learning for single image super-resolution:A brief review.
arXiv:1808.03344,2018
|
CSCD被引
2
次
|
|
|
|
|
14.
Kim S P. Recursive reconstruction of high resolution image from noisy under sampled multiframes.
IEEE Transactions on Acoustics Speech & Signal Processing,1990,38(6):1013-1027
|
CSCD被引
43
次
|
|
|
|
|
15.
Rhee S. Discrete cosine transform based regularized high-resolution image reconstruction algorithm.
Optical Engineering,1999,38(8):1348-1356
|
CSCD被引
10
次
|
|
|
|
|
16.
王相海. 小波域多角度轮廓模板变分模型的单幅图像超分辨率重建.
电子学报,2018,46(9):2256-2262
|
CSCD被引
7
次
|
|
|
|
|
17.
Nguyen N. An efficient wavelet-based algorithm for image super-resolution.
Proceedings of the 2000 International Conference on Image Processing,2000:351-354
|
CSCD被引
1
次
|
|
|
|
|
18.
Panagiotopoulou A. Super-resolution image reconstruction employing Kriging interpolation technique.
The 14th International Workshop on Systems,Signals and Image Processing and 6th EURASIP Conference focused on Speech and Image Processing,Multimedia Communications and Services,2007:144-147
|
CSCD被引
1
次
|
|
|
|
|
19.
Irani M. Super resolution from image sequences.
Proceedings of the 10th International Conference on Pattern Recognition,1990:115-120
|
CSCD被引
4
次
|
|
|
|
|
20.
Schultz R R. An Bayesian approach to image expansion for improved definition.
IEEE Transactions on Image Processing,1994,3(3):233-242
|
CSCD被引
81
次
|
|
|
|
|
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