[1] WANG Z, ZHAO T, CAI Y et al. Rapid, artifact-reduced, image reconstruction for superresolution structured illumination microscopy[J]. Innovation, 4, 100425(2023).

[2] SUN Z, TUITJE F, SPIELMANN C. A review of high-resolution microscopic ghost imaging with a low-dose pseudothermal light[J]. Journal of Microscopy, 284, 3-11(2021).

[3] YU H, LU R, HAN S et al. Fourier-transform ghost imaging with hard X rays[J]. Physical Review Letters, 117, 113901(2016).

[4] SCHORI A, SHWARTZ S. X-ray ghost imaging with a laboratory source[J]. Optics Express, 25, 14822-14828(2017).

[5] GONG W, ZHAO C, YU H et al. Three-dimensional ghost imaging lidar via sparsity constraint[J]. Scientific Reports, 6, 1-6(2016).

[6] MEI X, GONG W, YAN Y et al. Experimental research on prebuilt three dimensional imaging lidar[J]. Chinese Journal of Lasers, 43, 0710003(2016).

[7] DUARTE M F, DAVENPORT M A, TAKHAR D et al. Single-pixel imaging via compressive sampling[J]. IEEE Signal Processing Magazine, 25, 83-91(2008).

[8] KATZ O, BROMBERG Y, SILBERBERG Y. Compressive ghost imaging[J]. Applied Physics Letters, 95, 131110(2009).

[9] ASSMANN M, BAYER M. Compressive adaptive computational ghost imaging[J]. Scientific Reports, 3, 1545(2013).

[10] ZHONG Yajun, LIU Jiao, LIANG Wenqiang et al. Multiple speckle patterns differential compressive ghost imaging[J]. Acta Physica Sinica, 64, 014202(2015).

[11] CHEN Y, CHENG Z, FAN X et al. Compressive sensing ghost imaging based on image gradient[J]. Optik, 182, 1021-1029(2019).

[12] LI Y, WAN X. Compressive imaging beyond the sensor's physical resolution via coded exposure combined with time-delay integration[J]. Optics and Lasers in Engineering, 164, 107491(2023).

[13] LYU M, WANG W, WANG H et al. Deep-learning-based ghost imaging[J]. Scientific Reports, 7, 17865(2017).

[14] SHIMOBABA T, ENDO Y, NISHITSUJI T et al. Computational ghost imaging using deep learning[J]. Optics Communications, 413, 147-151(2018).

[15] RIZVI S, GAO J, ZHANG K et al. DeepGhost: real-time computational ghost imaging via deep learning[J]. Scientific Reports, 10, 11400(2018).

[16] BARBASTATHIS G, OZCAN A, SITU G. On the use of deep learning for computational imaging[J]. Optica, 6, 921-943(2019).

[17] REN Z, XU Z, LAM E. End-to-end deep learning framework for digital holographic reconstruction[J]. Advanced Photonics, 1, 016004(2019).

[18] GAO Z, CHENG X, CHEN K et al. Computational ghost imaging in scattering media using simulation-based deep learning[J]. IEEE Photonics Journal, 12, 1-15(2020).

[19] CHEN Y, SUN Z, LI C et al. Computational ghost imaging in turbulent water based on self-supervised information extraction network[J]. Optics & Laser Technology, 167, 109735(2023).

[20] CHANG X, WU Z, LI D et al. Self-supervised learning for single-pixel imaging via dual-domain constraints[J]. Optics Letters, 48, 1566-1569(2023).

[21] LI J, WU B, LIU T et al. URNet: high-quality single-pixel imaging with untrained reconstruction network[J]. Optics and Lasers in Engineering, 166, 107580(2023).

[22] LIU S, MENG X, YIN Y et al. Computational ghost imaging based on an untrained neural network[J]. Optics and Lasers in Engineering, 147, 106744(2021).

[23] WANG F, WANG C, CHEN M et al. Far-field super-resolution ghost imaging with a deep neural network constraint[J]. Light: Science & Applications, 11, 1-11(2022).

[24] WANG F, WANG C, DENG C et al. Single-pixel imaging using physics enhanced deep learning[J]. Photonics Research, 10, 104-110(2022).

[25] FAN C, PENG Y, CAO G et al. GaitPart:temporal part-based model for gait recognition[C], 14225-14233(2020).

[26] FERRI F, MAGATTI D, GATTI A. Differential ghost imaging[J]. Physical Review Letters, 104, 253603(2010).

[27] SUN Z, TUITJE F, SPIELMANN C. Improving the contrast of pseudothermal ghost images based on the measured signal distribution of speckle fields[J]. Applied Sciences, 11, 2621(2021).

[28] SUN Z, TUITJE F, SPIELMANN C. Toward high contrast and high-resolution microscopic ghost imaging[J]. Optics Express, 27, 33652-33661(2019).