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Portable 4D Snapshot Hyperspectral Imager for Fastspectral and Surface Morphology Measurements (Invited Paper)

By Jing Luo, Zijian Lin, Yuxin Xing, Erik Forsberg, Chengdong Wu, Xinhua Zhu, Tingbiao Guo, Gaoxuan Wang, Beilei Bian, Dun Wu, and Sailing He
Progress In Electromagnetics Research, Vol. 173, 25-36, 2022


A portable 4D snapshot hyperspectral imager (P4DS imager) with compact size, fast imaging time, low cost, and simple design is proposed and demonstrated. The key components of the system are a projector, a liquid crystal tunable filter (LCTF), and a camera. It has two operating modes dependent on the set state of the LCTF: a 3D light measurement mode that produces a 3D point cloud reconstruction of the object, and a hyperspectral imaging mode yielding spectral data. The camera imaging plane is the same for both operating modes allowing the collected spatial and spectral data to be directly fused into a 4D data set without post-processing. The P4DS imager has excellent performance with a spectral resolution of 10 nm, a spatial depth accuracy of 55.7 um, and total 4D imaging time of 0.8 s. 4D imaging experiments of three different samples, colored doll statue, green broccoli, and a human face, are presented to demonstrate the efficiency and applicability of the system. Due to being cost-effective, portable, and good imaging performance, the proposed system is suitable for commercialization and mass production.


Jing Luo, Zijian Lin, Yuxin Xing, Erik Forsberg, Chengdong Wu, Xinhua Zhu, Tingbiao Guo, Gaoxuan Wang, Beilei Bian, Dun Wu, and Sailing He, "Portable 4D Snapshot Hyperspectral Imager for Fastspectral and Surface Morphology Measurements (Invited Paper)," Progress In Electromagnetics Research, Vol. 173, 25-36, 2022.


    1. Li, Q. L., Y. T. Wang, H. Y. Liu, X. F. He, D. R. Xu, J. B. Wang, and F. M. Guo, "Leukocyte cells identification and quantitative morphometry based on molecular hyperspectral imaging technology," Computerized Medical Imaging and Graphics, Vol. 38, No. 3, 171-178, 2014.

    2. Yao, X., S. Li, and S. L. He, "Dual-mode hyperspectral bio-imager with a conjugated camera for quick object-selection and focusing," Progress In Electromagnetics Research, Vol. 168, 133-143, 2020.

    3. Liu, X. M., Z. Q. Jiang, T. C. Wang, F. H. Cai, and D. Wang, "Fast hyperspectral imager driven by a low-cost and compact galvo-mirror," Optik, Vol. 224, 165716, 2020.

    4. Wu, D. and D. W. Sun, "Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review - Part I: Fundamentals," Innovative Food Sci. Emerg. Technol., Vol. 19, 1-14, 2013.

    5. Zdoan, G., X. H. Lin, and D. W. Sun, "Rapid and noninvasive sensory analyses of food products by hyperspectral imaging: Recent application developments," Trends in Food Science & Technology, Vol. 111, 151-165, 2021.

    6. Ramakrishnan, D. and R. Bharti, "Hyperspectral remote sensing and geological applications," Current Science, Vol. 108, No. 5, 879-891, 2015.

    7. Sun, Y., Y. J. Zhao, K. Qin, J. T. Nie, and H. B. Li, "Geological application of HySpex ground hyperspectral remote sensing in gold and uranium ore deposits," Asia-Pacific Energy Equipment Engineering Research Conference (AP3ER), Vol. 9, 392-395, 2015.

    8. Courtenay, L. A., D. Gonzalez-Aguilera, S. Laguela, S. del Pozo, C. Ruiz-Mendez, I. Barbero-Garcia, C. Roman-Curto, J. Canueto, C. Santos-Duran, M. E. Cardenoso-Alvarez, M. Roncero-Riesco, D. Hernandez-Lopez, D. Guerrero-Sevilla, and P. Rodriguez-Gonzalvez, "Hyperspectral imaging and robust statistics in non-melanoma skin cancer analysis," Biomed. Opt. Express, Vol. 12, No. 8, 5107-5127, 2021.

    9. Dicker, D. T., J. Lerner, P. Van Belle, S. F. Barth, D. Guerry, M. Herlyn, and W. S. El-Deiry, "Differentiation of normal skin and melanoma using high resolution hyperspectral imaging," Cancer Biology & Therapy, Vol. 5, No. 8, 1033-1038, 2006.

    10. Manley, M., "Near-infrared spectroscopy and hyperspectral imaging: Non-destructive analysis of biological materials," Chem. Soc. Rev., Vol. 43, No. 24, 8200-8214, 2014.

    11. Studer, V., J. Bobin, M. Chahid, H. S. Mousavi, E. Candes, and M. Dahan, "Compressive fluorescence microscopy for biological and hyperspectral imaging," Proceedings of the National Academy of Sciences of the United States of America, Vol. 109, No. 26, E1679-E1687, 2012.

    12. Yang, Q. L., B. Niu, S. Q. Gu, J. G. Ma, C. M. Zhao, Q. Chen, D. H. Guo, X. J. Deng, Y. A. Yu, and F. Zhang, "Rapid detection of nonprotein nitrogen adulterants in milk powder using point-scan raman hyperspectral imaging technology," ACS Omega, Vol. 7, No. 2, 2064-2073, 2022.

    13. Yao, X. L., F. H. Cai, P. Y. Zhu, H. X. Fang, J. W. Li, and S. L. He, "Non-invasive and rapid pH monitoring for meat quality assessment using a low-cost portable hyperspectral scanner," Meat Sci., Vol. 152, 73-80, 2019.

    14. Gomez-Sanchis, J., J. Blasco, E. Soria-Olivas, D. Lorente, P. Escandell-Montero, J. M. Martinez-Martinez, M. Martinez-Sober, and N. Aleixos, "Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicilliumdigitatum and Penicilliumitalicum using the most relevant bands and non-linear classifiers," Postharvest Biol. Technol., Vol. 82, 76-86, 2013.

    15. Abdlaty, R., J. Orepoulos, P. Sinclair, R. Berman, and Q. Y. Fang, "High throughput AOTF hyperspectral imager for randomly polarized light," Photonics, Vol. 5, No. 1, 3, 2018.

    16. Gat, N., "Imaging spectroscopy using tunable filters: A review," Conference on Wavelet Applications VII, Vol. 4056, 50-64, 2000.

    17. Wang, X., Y. Zhang, X. Ma, T. Xu, and G. R. Arce, "Compressive spectral imaging system based on liquid crystal tunable filter," Opt. Express, Vol. 26, No. 19, 25226-25243, 2018.

    18. Gebhart, S. C., R. C. Thompson, and A. Mahadevan-Jansen, "Liquid-crystal tunable filter spectral imaging for brain tumor demarcation," Appl. Opt., Vol. 46, No. 10, 1896-1910, 2007.

    19. Nalpantidis, L., G. C. Sirakoulis, and A. Gasteratos, "Review of stereo vision algorithms: From software to hardware," Int. J. Optomechatronics, Vol. 2, No. 4, 435-462, 2008.

    20. Dhond, U. R. and J. K. Aggarwal, "Structure from stereo - A review," IEEE Transactions on Systems Man and Cybernetics, Vol. 19, No. 6, 1489-1510, 1989.

    21. Zhang, S., "High-speed 3D shape measurement with structured light methods: A review," Opt. Lasers Eng., Vol. 106, 119-131, 2018.

    22. Hyun, J. S., G. T. C. Chiu, and S. Zhang, "High-speed and high-accuracy 3D surface measurement using a mechanical projector," Opt. Express, Vol. 26, No. 2, 1474-1487, 2018.

    23. Foix, S., G. Alenya, and C. Torras, "Lock-in Time-of-Flight (ToF) cameras: A survey," IEEE Sens. J., Vol. 11, No. 9, 1917-1926, 2011.

    24. Luo, L. Q., X. Chen, Z. P. Xu, S. Li, Y. R. Sun, and S. L. He, "A parameter-free calibration process for a scheimpflug LIDAR for volumetric profiling," Progress In Electromagnetics Research, Vol. 169, 117-127, 2020.

    25. Zhong, K., Z. W. Li, X. H. Zhou, Y. F. Li, Y. S. Shi, and C. J. Wang, "Enhanced phase measurement profilometry for industrial 3D inspection automation," Int. J. Adv. Manuf. Technol., Vol. 76, No. 9-12, 1563-1574, 2015.

    26. Caudullo, P. T., "3D laser scanning: Technology at the service of the protection of cultural heritage," Archeomatica-Tecnologie Per I Beni Culturali, Vol. 11, No. 3, 6-9, 2020.

    27. Jahanshahi, M. R. and S. F. Masri, "Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures," Autom. Constr., Vol. 22, 567-576, 2012.

    28. Peng, H. C., Z. C. Ruan, F. H. Long, J. H. Simpson, and E. W. Myers, "V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets," Nat. Biotechnol., Vol. 28, No. 4, 348-375, 2010.

    29. Cai, F. H., T. C. Wang, J. J. Wu, and X. Y. Zhang, "Handheld four-dimensional optical sensor," Optik, Vol. 203, 164001, 2020.

    30. Aasen, H., A. Burkart, A. Bolten, and G. Bareth, "Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance," ISPRS J. Photogramm. Remote Sens., Vol. 108, 245-259, 2015.

    31. Zhao, H. J., Z. Y. Wang, G. R. Jia, X. D. Li, and Y. Zhang, "Field imaging system for hyperspectral data, 3D structural data and panchromatic image data measurement based on acousto-optic tunable filter," Opt. Express, Vol. 26, No. 13, 17717-17730, 2018.

    32. Zhao, H. J., L. B. Xu, S. G. Shi, H. Z. Jiang, and D. Chen, "A high throughput integrated hyperspectral imaging and 3D measurement system," Sensors, Vol. 18, No. 4, 1608, 2018.

    33. Heist, S., C. Zhang, K. Reichwald, P. Kuhmstedt, G. Notni, and A. Tuennermann, "5D hyperspectral imaging: Fast and accurate measurement of surface shape and spectral characteristics using structured light," Opt. Express, Vol. 26, No. 18, 23366-23379, 2018.

    34. Luo, J., S. Li, E. Forsberg, and S. L. He, "4D surface shape measurement system with high spectral resolution and great depth accuracy," Opt. Express, Vol. 29, No. 9, 13048-13070, 2021.

    35. Ivorra, E., S. Verdu, A. J. Sanchez, R. Grau, and J. M. Barat, "Predicting gilthead sea bream (Sparus aurata) freshness by a novel combined technique of 3D imaging and SW-NIR spectral analysis," Sensors, Vol. 16, No. 10, 1735, 2016.

    36. Kim, M. H., H. Rushmeier, J. Dorsey, T. A. Harvey, R. O. Prum, D. S. Kittle, and D. J. Brady, "3D imaging spectroscopy for measuring hyperspectral patterns on solid objects," ACM Trans. Graphics, Vol. 31, No. 4, 1-11, 2012.

    37. Chen, B. W., S. Shi, J. Sun, W. Gong, J. Yang, L. Du, K. H. Guo, B. H. Wang, and B. W. Chen, "Hyperspectral lidar point cloud segmentation based on geometric and spectral information," Opt. Express, Vol. 27, No. 17, 24043-24059, 2019.

    38. Li, J. Q., Y. Zheng, L. L. Liu, and B. W. Li, "4D line-scan hyperspectral imaging," Opt. Express, Vol. 29, No. 21, 34835-34849, 2021.

    39. Beeckman, J., K. Neyts, and P. J. M. Vanbrabant, "Liquid-crystal photonic applications," Opt. Eng., Vol. 50, No. 8, 081202, 2011.

    40. Aharon, O. and I. Abdulhalim, "Tunable optical filter having a large dynamic range," Opt. Lett., Vol. 34, No. 14, 2114-2116, 2009.

    41. Zuo, C., S. J. Feng, L. Huang, T. Y. Tao, W. Yin, and Q. Chen, "Phase shifting algorithms for fringe projection profilometry: A review," Opt. Lasers Eng., Vol. 109, 23-59, 2018.

    42. Reich, C., R. Ritter, and J. Thesing, "White light heterodyne principle for 3D-measurement," Proc. SPIE, Vol. 3100, No. 1, 236-244, 1997.

    43. Li, Z., Y. Shi, C. Wang, and Y. Wang, "Accurate calibration method for a structured light system," Opt. Eng., Vol. 47, No. 5, 053604, 2008.

    44. Bajguz, A. and A. Tretyn, "The chemical characteristic and distribution of brassinosteroids in plants," Phytochemistry, Vol. 62, No. 7, 1027-1046, 2003.

    45. Pilsl, U., F. Anderhuber, and S. Neugebauer, "The facial artery-the main blood vessel for the anterior face?," Dermatologic Surgery, Vol. 42, No. 2, 203-208, 2016.