• Infrared and Laser Engineering
  • Vol. 49, Issue 6, 20200010 (2020)
Feng Shi1, Tongxi Lu2, Shuning Yang1, Zhuang Miao1, Ye Yang1, Wenwen Zhang2, and Ruiqing He3、*
Author Affiliations
  • 1微光夜视技术重点实验室,陕西 西安 710065
  • 2南京理工大学 江苏省光谱成像和智能感知重点实验室,江苏 南京 210094
  • 3南京工程学院 信息与通信工程学院,江苏 南京 211167
  • show less
    DOI: 10.3788/IRLA20200010 Cite this Article
    Feng Shi, Tongxi Lu, Shuning Yang, Zhuang Miao, Ye Yang, Wenwen Zhang, Ruiqing He. Target recognition method based on single-pixel imaging system and deep learning in the noisy environment[J]. Infrared and Laser Engineering, 2020, 49(6): 20200010 Copy Citation Text show less
    References

    [1] T B Pittman, Y H Shih, D V Strekalov. Optical imaging by means of two-photon quantum entanglement. Physical Review A, 52, R3429(1995).

    [2] R S Bennink, S J Bentley, R W Boyd. “Two-photon” coincidence imaging with a classical source. Physical Review Letters, 89, 113601(2002).

    [3] F Ferri, D Magatti, V G Sala. Longitudinal coherence in thermal ghost imaging. Applied Physics Letters, 92, 261109(2008).

    [4] J H Shapiro. Computational ghost imaging. Physical Review A, 78, 061802(2008).

    [5] Y Bromberg, O Katz, Y Silberberg. Ghost imaging with a single detector. Physical Review A, 79, 053840(2009).

    [6] O Katz, Y Bromberg, Y Silberberg. Compressive ghost imaging. Applied Physics Letters, 95, 131110(2009).

    [7] F Ferri, D Magatti, L A Lugiato. Differential ghost imaging. Physical Review Letters, 104, 253603(2010).

    [8] M J Sun, J M Zhang. Single-pixel imaging and its application in three-dimensional reconstruction: a brief review. Sensors, 19, 732(2019).

    [9] B Sun, M P Edgar, R Bowman. 3D computational imaging with single-pixel detectors. Science, 340, 844-847(2013).

    [10] L Bian, J Suo, G Situ. Multispectral imaging using a single bucket detector. Scientific Reports, 6, 24752(2016).

    [11] V Studer, J Bobin, M Chahid. Compressive fluorescence microscopy for biological and hyperspectral imaging. Proceedings of the National Academy of Sciences, 109, E1679-E1687(2012).

    [12] G M Gibson, B Sun, M P Edgar. Real-time imaging of methane gas leaks using a single-pixel camera. Optics Express, 25, 2998-3005(2017).

    [13] C Zhao, W Gong, M Chen. Ghost imaging lidar via sparsity constraints. Applied Physics Letters, 101, 141123(2012).

    [14] E Li, Z Bo, M Chen. Ghost imaging of a moving target with an unknown constant speed. Applied Physics Letters, 104, 251120(2014).

    [15] W Gong, C Zhao, H Yu. Three-dimensional ghost imaging lidar via sparsity constraint. Scientific Reports, 6, 26133(2016).

    [16] M J Sun, M P Edgar, G M Gibson. Single-pixel three-dimensional imaging with time-based depth resolution. Nature Communications, 7, 1-6(2016).

    [17] W Chen, X Chen. Object authentication in computational ghost imaging with the realizations less than 5% of Nyquist limit. Optics Letters, 38, 546-548(2013).

    [18] J Wu, B Haobogedewude, Z Liu. Optical secure image verification system based on ghost imaging. Optics Communications, 399, 98-103(2017).

    [19] H Chen, J Shi, X Liu. Single-pixel non-imaging object recognition by means of Fourier spectrum acquisition. Optics Communications, 413, 269-275(2018).

    [20] S Ota, R Horisaki, Y Kawamura. Ghost cytometry. Science, 360, 1246-1251(2018).

    [21] S Jiao, J Feng, Y Gao. Optical machine learning with incoherent light and a single-pixel detector. Optics Letters, 44, 5186-5189(2019).

    [22] Zhang Z, Li X, Yao M, et al. Imagefree realtime classification of fast moving objects using learned spatial light modulation a singlepixel detect[J]. arXiv preprint arXiv: 1912.01974, 2019.

    [23] Nimento G, Laranjeira C, Braz V, et al. A robust indo scene recognition method based on sparse representation[C]Iberoamerican Congress on Pattern Recognition. Springer, Cham, 2017: 408415.

    [24] W Zhang, C Li, G Peng. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mechanical Systems and Signal Processing, 100, 439-453(2018).

    [25] Simonyan K, Zisserman A. Very deep convolutional wks f largescale image recognition[J]. arXiv preprint arXiv: 1409.1556, 2014.

    [26] Du J. Research of antinoise energyefficient deep neural wks f spontaneous facial expression recognition[D]. Guangzhou: Guangdong University of Technology, 2019. (in Chinese)

    [27] X Zhang, W Liu. Research on SAR target recognition based on convolutional neural networks. Electronic Measurement Technology, 41, 92-96(2018).

    [28] He K, Zhang X, Ren S, et al. Deep residual learning f image recognition[C] Proceedings of the IEEE Conference on Computer Vision Pattern Recognition. 2016: 770778.

    [29] Jiang S. Classification deion of fundus images based on Res[D]. Yantai: Shong Technology Business University, 2019. (in Chinese)

    [30] K Tang, Q He, Q Zhao, X Wang. Image recognition based on improved deep neural network. Journal of Nanjing Normal University (Natural Science Edition), 42, 115-121(2019).

    [31] K Wu, M Bai. Study on recognition algorithm for plaque in coronary CTA on the basis of deep residual network. China Medical Equipment, 16, 1-5(2019).

    [32] He K, Zhang X, Ren S, et al. Identity mappings in deep residual wks[C]European Conference on Computer Vision. Springer, Cham, 2016: 630645.

    CLP Journals

    [1] Wei Tan, Xianwei Huang, Teng Jiang, Qin Fu, Suqin Nan, Xuanpengfan Zou, Yanfeng Bai, Xiquan Fu. Research on the effect of noise-containing signal light on correlated imaging in complex environment (Invited)[J]. Infrared and Laser Engineering, 2021, 50(12): 20210657

    Feng Shi, Tongxi Lu, Shuning Yang, Zhuang Miao, Ye Yang, Wenwen Zhang, Ruiqing He. Target recognition method based on single-pixel imaging system and deep learning in the noisy environment[J]. Infrared and Laser Engineering, 2020, 49(6): 20200010
    Download Citation