| ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS |
Prev
Next
|
|
|
Handwritten digit recognition based on ghost imaging with deep learning |
| Xing He(何行)1, Sheng-Mei Zhao(赵生妹)1,2,†, and Le Wang(王乐)2 |
1 Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
2 Key Laboratory of Broadband Wireless Communication and Sensor Network Technology(Ministry of Education), Nanjing 210003, China |
|
|
|
|
Abstract We present a ghost handwritten digit recognition method for the unknown handwritten digits based on ghost imaging (GI) with deep neural network, where a few detection signals from the bucket detector, generated by the cosine transform speckle, are used as the characteristic information and the input of the designed deep neural network (DNN), and the output of the DNN is the classification. The results show that the proposed scheme has a higher recognition accuracy (as high as 98% for the simulations, and 91% for the experiments) with a smaller sampling ratio (say 12.76%). With the increase of the sampling ratio, the recognition accuracy is enhanced. Compared with the traditional recognition scheme using the same DNN structure, the proposed scheme has slightly better performance with a lower complexity and non-locality property. The proposed scheme provides a promising way for remote sensing.
|
Received: 10 August 2020
Revised: 17 November 2020
Accepted manuscript online: 11 December 2020
|
|
PACS:
|
42.30.Sy
|
(Pattern recognition)
|
| |
42.50.Ar
|
|
| |
42.30.-d
|
(Imaging and optical processing)
|
|
| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61871234 and 11847062). |
Corresponding Authors:
Sheng-Mei Zhao
E-mail: zhaosm@njupt.edu.cn
|
Cite this article:
Xing He(何行), Sheng-Mei Zhao(赵生妹), and Le Wang(王乐) Handwritten digit recognition based on ghost imaging with deep learning 2021 Chin. Phys. B 30 054201
|
[1] Cun Y L, Boser B, Denker J S, Henderson D and Jackel L D 1990 NIPS'89: Proceedings of the 2nd International Conference on Neural Information Processing Systems pp. 396-404
[2] Wang Y, Wang X and Liu W 2016 Inf. Sci. 351 67
[3] Goltsev A and Gritsenko V 2012 Neural Netw. 28 15
[4] Varatharajan R, Manogaran G and Priyan M K 2018 Multimed Tools Appl. 77 10195
[5] Kulkarni S R and Rajendran B 2018 Neural Netw. 103 118
[6] Li H X, Bai Y F, Shi X H, Nan S Q, Qu L J, Shen Q and Fu X Q 2017 Chin. Phys. B 26 104204
[7] Yin M Q, Wang L and Zhao S M 2019 Chin. Phys. B 28 094201
[8] Wang L, Zou L and Zhao S M 2018 Opt. Commun. 407 181
[9] Ren H D, Wang L and Zhao S M 2019 OSA Continuum 2 64
[10] Chen W and Chen X 2019 Appl. Phys. Lett. 104 251109
[11] Jiao S, Zhou C, Shi Y, Zou W and Li X 2019 Opt. Laser Technol. 109 370
[12] Zhang Y D and Zhao S M 2017 Chin. Phys. B 26 054205
[13] Zhao S, Wang L, Liang W, Cheng W and Gong L 2015 Opt. Commun. 353 90
[14] Strekalov D V, Sergienko A V, Klyshko D N and Shih Y H 1995 Phys. Rev. Lett. 74 3600
[15] Bennink R S, Bentley S J and Boyd R W 2002 Phys. Rev. Lett. 89 113601
[16] Valencia A, Scarcelli G, D'Angelo M and Shih Y 2005 Phys. Rev. Lett. 94 063601
[17] Shapiro J H 2008 Phys. Rev. A 78 061802
[18] Latorre-Carmona P, Traver V J, Sanchez J S and Tajahuerce E 2019 Image and Vision Computing 86 28
[19] Jiao S, Feng J, Gao Y, Lei T, Xie Z and Yuan X 2019 Opt. Lett. 44 5186
[20] Chen H, Shi J, Liu X, Niu Z and Zeng G 2018 Opt. Commun. 413 269
[21] Xu Y and Kelly K F 2019 arXiv:1901.09983 [cs.CV]
[22] Bacca J, Correa C V, Vargas E, Castillo S and Arguello H 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), October 13-16, 2019, Pittsburgh, PA, p. 1
[23] Zhong J, Zhang Z, Li X, Zheng S and Zheng G 2020 Opt. Express 9 28
[24] Ahmed N, Natarajan T, Rao and K R 1974 IEEE Trans. Comput. 23 90
[25] Kingma D P and Ba J L 2014 arXiv:1412.6980v8 [cs.LG]
[26] Wen Z, He B, Kotagiri R, Lu S and Shi J 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), May 21-25, 2018, Vancouver, Canada, p. 234
[27] Dogru N and Subasi A 2018 15th Learning and Technology Conference, February 25-26, 2018, Jeddah, Saudi Arabia, p. 40
[28] Wang F, Wang H, Haichao W, Li G and Situ G 2019 Opt. Express 27 25560
[29] Devi R G and Sumanjani P 2015 IEEE International Conference on Engineering and Technology (ICETECH), March 20, 2015, Coimbatore, India, p. 95 |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|
