| ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS |
Prev
Next
|
|
|
Computational ghost imaging with deep compressed sensing |
| Hao Zhang(张浩)1, Yunjie Xia(夏云杰)1,2,†, and Deyang Duan(段德洋)1,2,‡ |
1 School of Physics and Physical Engineering, Qufu Normal University, Qufu 273165, China;
2 Shandong Provincial Key Laboratory of Laser Polarization and Information Technology, Research Institute of Laser, Qufu Normal University, Qufu 273165, China |
|
|
|
|
Abstract Computational ghost imaging (CGI) provides an elegant framework for indirect imaging, but its application has been restricted by low imaging performance. Herein, we propose a novel approach that significantly improves the imaging performance of CGI. In this scheme, we optimize the conventional CGI data processing algorithm by using a novel compressed sensing (CS) algorithm based on a deep convolution generative adversarial network (DCGAN). CS is used to process the data output by a conventional CGI device. The processed data are trained by a DCGAN to reconstruct the image. Qualitative and quantitative results show that this method significantly improves the quality of reconstructed images by jointly training a generator and the optimization process for reconstruction via meta-learning. Moreover, the background noise can be eliminated well by this method.
|
Received: 22 March 2021
Revised: 22 April 2021
Accepted manuscript online: 12 May 2021
|
|
PACS:
|
42.30.Va
|
(Image forming and processing)
|
| |
42.50.-p
|
(Quantum optics)
|
|
| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11704221, 11574178, and 61675115) and the Taishan Scholar Project of Shandong Province, China (Grant No. tsqn201812059). |
Corresponding Authors:
Yunjie Xia, Deyang Duan
E-mail: yjxia@qfnu.edu.cn;duandy2015@qfnu.edu.cn
|
Cite this article:
Hao Zhang(张浩), Yunjie Xia(夏云杰), and Deyang Duan(段德洋) Computational ghost imaging with deep compressed sensing 2021 Chin. Phys. B 30 124209
|
[1] Pittman T B, Shih Y H, Strekalov D V and Sergienko A V 1995 Phys. Rev. A 52 R3429
[2] Shapiro J H and Boyd R W 2012 Quantum Inf. Process. 11 949
[3] Erkmen B I and Shapiro J H 2010 Adv. Opt. Photon. 2 405
[4] Meyers R E, Deacon K S and Shih Y 2011 App. Phys. Lett. 98 111115
[5] Li M F, Yan L, Yang R, Kou J and Liu S 2019 Acta Phys. Sin. 68 094204
[6] Pelliccia D, Rack A, Scheel M, Cantelli V and Paganin D M 2016 Phys. Rev. Lett. 117 113902
[7] Yu H, Lu R, Han S, Xie H, Du G, Xiao T and Zhu D 2016 Phys. Rev. Lett. 117 113901
[8] Zhang A X, He Y, Wu L A, Chen L and Wang B 2018 Optica 5 374
[9] Duan D Y, Zhang L and Xia Y J 2014 J. Opt. Soc. Am A 31 730
[10] Duan D Y and Xia Y J 2021 Opt. Express 29 4978
[11] Shapiro J H 2008 Phys. Rev. A 78 061802
[12] Wang C, Lan R J, Ren C and Cao D Z 2020 Phys. Rev A 101 033819
[13] Gong W L, Zhao C Q, Yu H, Chen M L, Xu W D and Han S S 2016 Sci. Rep. 6 26133
[14] Zhao C Q, Gong W L, Chen M L, Li E R, Wang H, Xu W D and Han S S 2012 Appl. Phys. Lett. 101 141123
[15] Erkmen B I 2012 J. Opt. Soc. A 29 782
[16] Duan D Y, Man Z X and Xia Y J 2019 Opt. Express 27 25187
[17] Katza O, Bromberg Y and Silberberg Y 2009 Appl. Phys. Lett. 95 131110
[18] Katkovnik V and Astola J 2012 J. Opt. Soc. Am. A 29 1556
[19] Yu W K, Li M F, Yao X R, Liu X F, Wu L A and Zhai G J 2014 Opt. Express 22 7133
[20] Chen Z, Shi J and Zeng G 2016 Appl. Opt. 55 8644
[21] Lyu M, Wang W, Wang H, Wang W, Li G, Chen N and Situ G H 2017 Sci. Rep. 7 17865
[22] He Y, Wang G, Dong G, Zhu S, Chen H, Zhang A and Xu Z 2018 Sci. Rep. 8 6469
[23] Shimobaba T, Endo Y, Nishitsuji T, Takahashi T, Nagahama Y, Hasegawa T, Sano M, Hirayama R, Kakue T, Shiraki A and Ito T 2018 Opt. Commun. 413 147
[24] Barbastathis G, Ozcan A and Situ G 2019 Optica 6 921
[25] Wu Y, Rosca M and Lillicrap T 2019 arXiv:1905.06723v2
[26] Bromberg Y, Katz O and Silberberg Y 2009 Phys. Rev. A 79 053840
[27] Jiang W J, Li X Y, Peng X L and Sun B Q 2020 Opt. Express 28 7889
[28] Huang X J 2020 Electron. Commer. R. A 4 21 |
| 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
|
|
|
