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10.1038/s41598-017-18171-7SCIBasano L, 2006, APPL PHYS LETT, V89, DOI 10.1063/1.2338657; Candes EJ, 2006, COMMUN PUR APPL MATH, V59, P1207, DOI 10.1002/cpa.20124; Chen H, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.023808; Cheng J, 2009, OPT EXPRESS, V17, P7916, DOI 10.1364/OE.17.007916; Deng L., 2012, IEEE SIGNAL PROCESSI, V29, P141; Ferri F, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.253603; Horisaki R, 2016, OPT EXPRESS, V24, P13738, DOI 10.1364/OE.24.013738; Hu XM, 2015, OPT EXPRESS, V23, P11092, DOI 10.1364/OE.23.011092; Katkovnik V, 2012, J OPT SOC AM A, V29, P1556, DOI 10.1364/JOSAA.29.001556; Katz O, 2009, APPL PHYS LETT, V95, DOI 10.1063/1.3238296; Kingma D., 2014, 14126980 ARXIV; Krizhevsky A, 2012, ADV NEUR INFORM PROC, V25, P1097, DOI DOI 10.1145/3065386; LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539; Li C., 2009, CAAM REPORT, V20, P46; Li JH, 2017, SCI BULL, V62, P717, DOI 10.1016/j.scib.2017.04.008; Li S., 2017, ARXIV171106810; Lyu M., 2017, ARXIV170807881; Morris P. A., 2015, NATURE COMMUN, V6; Nair V, 2010, P 27 INT C MACH LEAR, V27, P807, DOI DOI 10.0RG/PAPERS/432.PDF; Pelliccia D, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.113902; PITTMAN TB, 1995, PHYS REV A, V52, pR3429, DOI 10.1103/PhysRevA.52.R3429; Rivenson Y., 2017, ARXIV170504286; Scarcelli G, 2006, APPL PHYS LETT, V88, DOI 10.1063/1.2172410; Shapiro JH, 2008, PHYS REV A, V78, DOI 10.1103/PhysRevA.78.061802; Sinha A, 2017, OPTICA, V4, P1117, DOI 10.1364/OPTICA.4.001117; Sun MJ, 2015, APPL OPTICS, V54, P7494, DOI 10.1364/AO.54.007494; Valencia A, 2005, PHYS REV LETT, V94, DOI 10.1103/PhysRevLett.94.063601; Wang W, 2015, OPT EXPRESS, V23, P28416, DOI 10.1364/OE.23.028416; Wang W, 2014, OPT LETT, V39, P5150, DOI 10.1364/OL.39.005150; Wu G, 2016, OPT LETT, V41, P2561, DOI 10.1364/OL.41.002561; Wu H, 2016, LASER PHYS LETT, V13, DOI 10.1088/1612-2011/13/5/055101; Yu H, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.113901; Zhao CQ, 2012, APPL PHYS LETT, V101, DOI 10.1063/1.47578743362047787Sci Rep2017REORTSOM21745In this manuscript, we propose a novel framework of computational ghost imaging, i.e., ghost imaging using deep learning (GIDL). With a set of images reconstructed using traditional Gl and the corresponding ground-truth counterparts, a deep neural network was trained so that it can learn the sensing model and increase the quality image reconstruction. Moreover, detailed comparisons between the image reconstructed using deep learning and compressive sensing shows that the proposed GIDL has a much better performance in extremely low sampling rate. Numerical simulations and optical experiments were carried out for the demonstration of the proposed GIDL.Deep-learning-based ghost imaging期刊论文EnglishLyu, Meng; Wang, Wei; Wang, Hao; Wang, Haichao; Li, Guowei; Chen, Ni; Situ, Guohai17865 WOS:000418359600115
外文题目: Deep-learning-based ghost imaging
作者: Lyu, Meng; Wang, Wei; Wang, Hao; Wang, Haichao; Li, Guowei; Chen, Ni; Situ, Guohai
刊名: Sci Rep
年: 2017 卷: 7 文章编号:17865
英文摘要:
文献类型: 期刊论文
正文语种: English
收录类别: SCI  
DOI: 10.1038/s41598-017-18171-7
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