A Review of Single Image Super-resolution Reconstruction Algorithms Based on Deep Learning

被引：0
作者：
Li J.-X. [1 ]
Zhao Y.-X. [2 ,3 ]
Wang J.-H. [1 ,4 ]
机构：
[1] College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun
[2] Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun
[3] University of Chinese Academy of Sciences, Beijing
[4] Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2021年 / 47卷 / 10期
关键词：
Computer vision; Deep learning; Neural network; Single image super-resolution (SISR);
D O I：
10.16383/j.aas.c190859
中图分类号：
学科分类号：
摘要：
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. Copyright © 2021 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：2341 / 2363
页数：22
相关论文
共 100 条
[11]  
Zeyde R, Elad M, Protter M., On single image scale-up using sparse-representations, Proceedings of the 7th International Conference on Curves and Surfaces, pp. 711-730, (2010)
[12]  
Huang J B, Singh A, Ahuja N., Single image super-resolution from transformed self-exemplars, Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197-5206, (2015)
[13]  
Dong C, Loy C C, Tang X O., Accelerating the super-resolution convolutional neural network, Proceedings of the 14th European Conference on Computer Vision, pp. 391-407, (2016)
[14]  
Martin D, Fowlkes C, Tal D, Malik J., A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, Proceedings of the 8th IEEE International Conference on Computer Vision (ICCV), pp. 416-423, (2001)
[15]  
Arbelaez P, Maire M, Fowlkes C, Malik J., Contour detection and hierarchical image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 5, pp. 898-916, (2011)
[16]  
Fujimoto A, Ogawa T, Yamamoto K, Matsui Y, Aizawa K., Manga109 dataset and creation of metadata, Proceedings of the 1st International Workshop on coMics ANalysis, Processing and Understanding, pp. 1-5, (2016)
[17]  
Yang J C, Wright J, Huang T S, Ma Y., Image super-resolution via sparse representation, IEEE Transactions on Image Processing, 19, 11, pp. 2861-2873, (2010)
[18]  
Agustsson E, Timofte R., NTIRE 2017 challenge on single image super-resolution: Dataset and study, Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1122-1131, (2017)
[19]  
Timofte R, Rothe R, Van Gool L., Seven ways to improve example-based single image super resolution, Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1865-1873, (2016)
[20]  
Wang X T, Yu K, Dong C, Loy C C., Recovering realistic texture in image super-resolution by deep spatial feature transform, Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 606-615, (2018)
12345678910