Progress on X-ray diffraction imaging via intensity correlation

doi:10.3969/j.issn.1007-5461.2022.06.003

Abstract

Abstract: Diffraction imaging via intensity correlation is a novel diffraction imaging method based on the high-order correlation characteristics of optical field. The Fourier information of a sample can be obtained nonlocally by calculating the correlation between the intensity fluctuation of the reference beam and the test beam. As a new imaging method, diffraction imaging via intensity correlation has the advantages of nonlocal imaging, low radiation and high resolution and so on, thus it can solve problems that are difficult to handle by conventional imaging. This technology has attracted much attention recently, and has great potential in biomedicine, material science and many other fields in the future. In this paper, the latest progress of diffraction imaging via intensity correlation is briefly reviewed, and some related image reconstruction algorithms are introduced. Finally, the existing problems of this method are pointed out and its future development is prospected.

Key words: quantum optics, imaging systems, ghost imaging, diffraction imaging, image reconstruction

CLC Number:

O431.2

TAN Zhijie , YANG Hairui , , YU Hong , , HAN Shensheng , ∗. Progress on X-ray diffraction imaging via intensity correlation[J]. Chinese Journal of Quantum Electronics, 2022, 39(6): 851-862.

References

[1] Pittman TB, Shih YH, Strekalov D V, et al. Optical Imaging by Means of Two-Photon Quantum Entanglement[J]. Physical Review A, 1995, 52(5): R3429–R3432.
[2] Bennink RS, Bentley SJ, Boyd RW. ‘Two-Photon’ Coincidence Imaging with a Classical Source[J]. Phys Rev Lett, 2002, 89(11): 113601.
[3] Garbe U, Ahuja Y, Ibrahim R, et al. Industrial Application Experiments on the Neutron Imaging Instrument DINGO[J]. Physics Procedia, 2017, 88(September 2016): 13–18.
[4] Gatti A, Brambilla E, Bache M, et al. Ghost Imaging with Thermal Light: Comparing Entanglement and ClassicalCorrelation[J]. Physical Review Letters, 2004, 93(9): 093602.
[5] Zhang D, Zhai YH, Wu LA, et al. Correlated Two-Photon Imaging with True Thermal Llight[J]. Optics Letters, 2005, 30(18): 2354–2356.
[6] Tian N, Guo Q, Wang A, et al. Fluorescence Ghost Imaging with Pseudothermal Light[J]. Optics Letters, 2011, 36(16): 3302–3304.
[7] Martienssen W, Spiller E. Coherence and Fluctuations in Light Beams[J]. American Journal of Physics, 1964, 32(12): 919–926.
[8] Shih Y. The Physics of Ghost Imaging: Nonlocal Interference or Local Intensity Fluctuation Correlation?[J]. Quantum Information Processing, 2012, 11(4): 995–1001.
[9] Strekalov D V., Sergienko A V., Klyshko DN, et al. Observation of Two-Photon “Ghost” Interference and Diffraction[J]. Physical Review Letters, 1995, 74(18): 3600–3603.
[10] Bo Z, Gong W, Han S. Phase-Retrieval Ghost Imaging of Complex-Valued Objects[J]. Physical Review A - Atomic, Molecular, and Optical Physics, 2010, 82(2): 1–4.
[11] Khakimov RI, Henson BM, Shin DK, et al. Ghost Imaging with Atoms[J]. Nature, 2016, 540(7631): 100–103.
[12] Kingston AM, Myers GR, Pelliccia D, et al. Neutron Ghost Imaging[J]. Physical Review A, 2020, 101(5): 053844.
[13] Li S, Cropp F, Kabra K, et al. Electron Ghost Imaging[J]. Physical Review Letters, 2018, 121(11): 114801.
[14] Chen BK, Sidorenko P, Lahav O, et al. Multiplexed Single-Shot Ptychography[J]. Optics Letters, 2018, 43(21): 5379.
[15] Miao J, Charalambous P, Kirz J, et al. Extending the Methodology of X-Ray Crystallography to Allow Imaging of Micrometre-Sized Non-Crystalline Specimens[J]. Nature, 1999, 400(6742): 342–344.
[16] Xu R, Jiang H, Song C, et al. Single-Shot Three-Dimensional Structure Determination of Nanocrystals with Femtosecond X-Ray Free-Electron Laser Pulses[J]. Nature Communications, 2014, 5(May): 1–9.
[17] Ekeberg T, Svenda M, Abergel C, et al. Three-Dimensional Reconstruction of the Giant Mimivirus Particle with an X-Ray Free-Electron Laser[J]. Physical Review Letters, 2015, 114(9): 1–6.
[18] He S, Shen X, Wang H, et al. Ghost Diffraction without a Beamsplitter[J]. Applied Physics Letters, 2010, 96(18): 181108.
[19] Gatti A, Magatti D, Bache M, et al. High-Resolution Ghost Image and Ghost Diffraction Experiments with Incoherent Pseudo-Thermal Light[J]. Quantum Communications and Quantum Imaging III, 2005, 5893(August): 58930D.
[20] Cheng J, Han S. Incoherent Coincidence Imaging and Its Applicability in X-Ray Diffraction[J]. Physical Review Letters, 2004, 92(9): 093903.
[21] Zhang M, Wei Q, Shen X, et al. Lensless Fourier-Transform Ghost Imaging with Classical Incoherent Light[J]. Physical Review A, 2007, 75(2): 441–445.
[22] Yu H, Lu R, Han S, et al. Fourier-Transform Ghost Imaging with Hard X Rays[J]. Physical Review Letters, 2016, 117(11): 113901.
[23] Shapiro JH. Computational Ghost Imaging[C]//Conference on Lasers and Electro-Optics/International Quantum Electronics Conference. Washington, D.C.: OSA, 2009: IThK7.
[24] Mait JN, Euliss GW, Athale RA. Computational Imaging[J]. Advances in Optics and Photonics, 2018, 10(2): 409–483.
[25] Katz O, Bromberg Y, Silberberg Y. Compressive Ghost Imaging[J]. Applied Physics Letters, 2009, 95(13): 131110.
[26] Zhu R, Yu H, Lu R, et al. Spatial Multiplexing Reconstruction for Fourier-Transform Ghost Imaging via Sparsity Constraints[J]. Optics Express, 2018, 26(3): 2181.
[27] Liu P, Zhang H, Zhang K, et al. Multi-Level Wavelet-CNN for Image Restoration[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. .
[28] Lyu M, Wang W, Wang HH, et al. Deep-Learning-Based Ghost Imaging[J]. Scientific Reports, 2017, 7(1): 17865.
[29] Sinha A, Lee J, Li S, et al. Lensless Computational Imaging through Deep Learning[J]. Optica, 2017, 4(9): 1117.
[30] Zhao Xin, Yu Hong, Lu Ronghua, et al. Research on Pseudo-Thermal Source of X-Ray Fourier-Transform Ghost Imaging[J]. Acta Optica Sinica, 2017, 37(5): 0511001(in chinese).
赵鑫，喻虹，陆荣华，等. X光傅里叶变换关联成像赝热光源研究[J]. 光学学报，2017, 37(5): 0511001.
[31] Wang H, Han S. Coherent Ghost Imaging Based on Sparsity Constraint without Phase-Sensitive Detection[J]. EPL (Europhysics Letters), 2012, 98(2): 24003.
[32] Tan Z, Yu H, Lu R, et al. Non-Locally Coded Fourier-Transform Ghost Imaging[J]. Optics Express, 2019, 27(3): 2937.
[33] Candès EJ, Strohmer T, Voroninski V, et al. PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming[J]. Communications on Pure and Applied Mathematics, 2013, 66(8): 1241–1274.
[34] Candes EJ, Eldar YC, Strohmer T, et al. Phase Retrieval via Matrix Completion[J]. SIAM Review, 2015, 57(2): 225–251.
[35] Goldstein T, Studer C. PhaseMax: Convex Phase Retrieval via Basis Pursuit[J]. IEEE Transactions on Information Theory, 2018, 64(4): 2675–2689.
[36] Wei Z, Chen W, Yin T, et al. Robust Phase Retrieval of Complex-Valued Object in Phase Modulation by Hybrid Wirtinger Flow Method[J]. Optical Engineering, 2017, 56(09): 1.
[37] Chen Y, Candès EJ. Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems[J]. Communications on Pure and Applied Mathematics, 2017, 70(5): 822–883.
[38] Candes EJ, Li X, Soltanolkotabi M. Phase Retrieval via Wirtinger Flow: Theory and Algorithms[J]. IEEE Transactions on Information Theory, 2015, 61(4): 1985–2007.
[39] Candès EJ, Li X, Soltanolkotabi M, et al. Phase Retrieval from Coded Diffraction Patterns[J]. Applied and Computational Harmonic Analysis, 2015, 39(2): 277–299.
[40] Zhang F, Chen B, Morrison GR, et al. Phase Retrieval by Coherent Modulation Imaging[J]. Nature Communications, 2016, 7(1): 13367.
[41] Liu Z, Tan S, Wu J, et al. Spectral Camera Based on Ghost Imaging via Sparsity Constraints[J]. Scientific Reports, 2016, 6(1): 25718.
[42] Yu W-K, Li M-F, Yao X-R, et al. Adaptive Compressive Ghost Imaging Based on Wavelet Trees and Sparse Representation[J]. Optics Express, 2014, 22(6): 7133.
[43] Gong W, Zhao C, Yu H, et al. Three-Dimensional Ghost Imaging Lidar via Sparsity Constraint[J]. Scientific Reports 2016 6:1, 2016, 6(1): 1–6.
[44] Xi M, Chen H, Yuan Y, et al. Bi-Frequency 3D Ghost Imaging with Haar Wavelet Transform[J]. Optics Express, 2019, 27(22): 32349.
[45] Huang H, Zhou C, Tian T, et al. High-Quality Compressive Ghost Imaging[J]. Optics Communications, 2018, 412(December 2019): 60–65.
[46] Yi C, Zhengdong C, Xiang F, et al. Compressive Sensing Ghost Imaging Based on Image Gradient[J]. Optik, 2019, 182: 1021–1029.
[47] Liu D, Li L, Chen H, et al. Complementary Normalized Compressive Ghost Imaging with Entangled Photons[J]. IEEE Photonics Journal, 2018, 10(2).
[48] Fienup JR. Phase Retrieval Algorithms: A Comparison[J]. Applied Optics, 1982, 21(15): 2758–2769.
[49] Quan TM, Nguyen-Duc T, Jeong WK. Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss[J]. IEEE Transactions on Medical Imaging, 2018, 37(6): 1488–1497.
[50] Horisaki R, Takagi R, Tanida J. Learning-Based Imaging through Scattering Media[J]. Optics Express, 2016, 24(13): 13738.
[51] Sinha A, Barbastathis G, Lee J, et al. Imaging through Glass Diffusers Using Densely Connected Convolutional Networks[J]. Optica, Vol. 5, Issue 7, Pp. 803-813, 2018, 5(7): 803–813.
[52] Li Y, Xue Y, Tian L. Deep Speckle Correlation: A Deep Learning Approach toward Scalable Imaging through Scattering Media[J]. Optica, 2018, 5(10): 1181.
[53] Ledig C, Theis L, Huszar F, et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017: 105–114.
[54] Dong C, Loy CC, He K, et al. Learning a Deep Convolutional Network for Image Super-Resolution[M]//Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, 2014: 184–199.
[55] Nehme E, Weiss LE, Michaeli T, et al. Deep-STORM: Super-Resolution Single-Molecule Microscopy by Deep Learning[J]. Optica, 2018, 5(4): 458.
[56] Nguyen T, Xue Y, Li Y, et al. Deep Learning Approach for Fourier Ptychography Microscopy[J]. Optics Express, 2018, 26(20): 26470.
[57] Khoshaman A, Vinci W, Denis B, et al. Quantum Variational Autoencoder[J]. Quantum Science and Technology, 2018, 4(1): 014001.
[58] Cemgil T. The Autoencoding Variational Autoencoder[J]. Advances in Neural Information Processing Systems, 2020.
[59] Pu Y, Gan Z, Henao R, et al. Variational Autoencoder for Deep Learning of Images, Labels and Captions[J]. Advances in Neural Information Processing Systems, 2016, 29.
[60] Hou X, Shen L, Sun K, et al. Deep Feature Consistent Variational Autoencoder[C]//2017 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2017: 1133–1141.
[61] Zhu R, Yu H, Tan Z, et al. Ghost Imaging Based on Y-Net: A Dynamic Coding and Decoding Approach[J]. Optics Express, 2020, 28(12): 17556.
[62] Shechtman Y, Sahl SJ, Backer AS, et al. Optimal Point Spread Function Design for 3D Imaging[J]. Physical Review Letters, 2014, 113(3): 133902.
[63] Sengupta SK, Kay SM. Fundamentals of Statistical Signal Processing: Estimation Theory[J]. Technometrics, 1995, 37(4): 465.
[64] Hu C, Zhu R, Yu H, et al. Correspondence Fourier-Transform Ghost Imaging[J]. Physical Review A, 2021, 103(4): 43717.
[65] Oppel S, Büttner T, Kok P, et al. Superresolving Multiphoton Interferences with Independent Light Sources[J]. Physical Review Letters, 2012, 109(23): 3–6.
[66] Classen A, Waldmann F, Giebel S, et al. Superresolving Imaging of Arbitrary One-Dimensional Arrays of Thermal Light Sources Using Multiphoton Interference[J]. Physical Review Letters, 2016, 117(25): 1–7.
[67] Sataloff RT, Johns MM, Kost KM. Quantum Imaging with Incoherent Photons[J]. Physical Review Letters, 2007, 99.
[68] Oberthür D. Biological Single-Particle Imaging Using XFELs-towards the next Resolution Revolution[J]. IUCrJ, 2018, 5: 663–666.
[69] Barty A. Single Molecule Imaging Using X-Ray Free Electron Lasers[J]. Current Opinion in Structural Biology, 2016, 40: 186–194.
[70] Schneider R, Mehringer T, Mercurio G, et al. Quantum Imaging with Incoherently Scattered Light from a Free-Electron Laser[J]. Nature Physics, 2018, 14(2): 126–129.
[71] Li Z, Medvedev N, Chapman HN, et al. Radiation Damage Free Ghost Diffraction with Atomic Resolution[J]. Journal of Physics B: Atomic, Molecular and Optical Physics, 2018, 51(2).
[72] Tamasaku K, Sawada K, Nishibori E, et al. Visualizing the Local Optical Response to Extreme-Ultraviolet Radiation with a Resolution of λ/380[J]. Nature Physics, 2011, 7(9): 705–708.
[73] Glover TE, Fritz DM, Cammarata M, et al. X-Ray and Optical Wave Mixing[J]. Nature, 2012, 488(7413): 603–608.
[74] Shwartz S, Coffee RN, Feldkamp JM, et al. X-Ray Parametric down-Conversion in the Langevin Regime[J]. Physical Review Letters, 2012, 109(1): 1–5.
[75] Lee K, Park Y. Exploiting the Speckle-Correlation Scattering Matrix for a Compact Reference-Free Holographic Image Sensor[J]. Nature Communications, 2016, 7(1): 1–7.
[76] Naik DN, Singh RK, Ezawa T, et al. Photon Correlation Holography[J]. Optics Express, 2011, 19(2): 1408.
[77] Vinu R V., Chen Z, Singh RK, et al. Ghost Diffraction Holographic Microscopy[J]. Optica, 2020, 7(12): 1697.
[78] Zhang Z, Ma X, Zhong J. Single-Pixel Imaging by Means of Fourier Spectrum Acquisition[J]. Nature Communications, 2015, 6(1): 6225.
[79] Mahdi Khamoushi SM, Nosrati Y, Tavassoli SH. Sinusoidal Ghost Imaging[J]. Optics Letters, 2015, 40(15): 3452.
[80] Lane TJ, Ratner D. What Are the Advantages of Ghost Imaging? Multiplexing for x-Ray and Electron Imaging[J]. Optics Express, 2020, 28(5): 5898.