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Seeing Through a Black Box: Toward High-Quality Terahertz Imaging via Subspace-and-Attention Guided Restoration

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13667))

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Abstract

Terahertz (THz) imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for object exploration and inspection. However, its strong water absorption nature and low noise tolerance lead to undesired blurs and distortions of reconstructed THz images. The performances of existing restoration methods are highly constrained by the diffraction-limited THz signals. To address the problem, we propose a novel Subspace-Attention-guided Restoration Network (SARNet) that fuses multi-spectral features of a THz image for effective restoration. To this end, SARNet uses multi-scale branches to extract spatio-spectral features of amplitude and phase which are then fused via shared subspace projection and attention guidance. Here, we experimentally construct a THz time-domain spectroscopy system covering a broad frequency range from 0.1 THz to 4 THz for building up temporal/spectral/spatial/phase/material THz database of hidden 3D objects. Complementary to a quantitative evaluation, we demonstrate the effectiveness of SARNet on 3D THz tomographic reconstruction applications.

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References

  1. Abbas, A., Abdelsamea, M., Gaber, M.: Classification of COVID-19 in chest x-ray images using DeTraC deep convolutional neural network. Appl. Intell. 51(2), 854–864 (2021)

    Article  Google Scholar 

  2. Bowman, T., et al.: Pulsed terahertz imaging of breast cancer in freshly excised murine tumors. J. Biomed. Opt. 23(2), 026004 (2018)

    Article  Google Scholar 

  3. braham, E., Younus, A., Delagnes, T.C., Mounaix, P.: Non-invasive investigation of art paintings by terahertz imaging. Appl. Phys. A 100(3), 585–590 (2010)

    Google Scholar 

  4. Calvin, Y., Shuting, F., Yiwen, S., Emma, P.M.: The potential of terahertz imaging for cancer diagnosis: a review of investigations to date. Quant. Imaging Med. Surg. 2(1), 33 (2012)

    Google Scholar 

  5. Cheng, S., Wang, Y., Huang, H., Liu, D., Fan, H., Liu, S.: NBNet: noise basis learning for image denoising with subspace projection. In: Proceedings of IEEE/CVF International Conference on Computer Vision and Pattern Recognition, pp. 4896–4906 (2021)

    Google Scholar 

  6. Dorney, T.D., Baraniuk, R.G., Mittleman, D.M.: Material parameter estimation with terahertz time-domain spectroscopy. JOSA A 18(7), 1562–1571 (2001)

    Article  Google Scholar 

  7. Fukunaga, K.: THz Technology Applied to Cultural Heritage in Practice. CHS, Springer, Tokyo (2016). https://doi.org/10.1007/978-4-431-55885-9

    Book  Google Scholar 

  8. Geladi, P., Burger, J., Lestander, T.: Hyperspectral imaging: calibration problems and solutions. Chemom. Intell. Lab. Syst. 72(2), 209–217 (2004)

    Article  Google Scholar 

  9. de Gonzalez, A.B., Darby, S.: Risk of cancer from diagnostic x-rays: estimates for the UK and 14 other countries. Lancet 363(9406), 345–351 (2004)

    Article  Google Scholar 

  10. Hung, Y.C., Yang, S.H.: Kernel size characterization for deep learning terahertz tomography. In: Proc. IEEE International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), pp. 1–2 (2019)

    Google Scholar 

  11. Hung, Y.C., Yang, S.H.: Terahertz deep learning computed tomography. In: Proceedings of International Infrared, Millimeter, and Terahertz Waves, pp. 1–2. IEEE (2019)

    Google Scholar 

  12. Janke, C., Först, M., Nagel, M., Kurz, H., Bartels, A.: Asynchronous optical sampling for high-speed characterization of integrated resonant terahertz sensors. Opt. Lett. 30(11), 1405–1407 (2005)

    Article  Google Scholar 

  13. Jansen, C., et al.: Terahertz imaging: applications and perspectives. Appl. Opt. 49(19), E48–E57 (2010)

    Article  Google Scholar 

  14. Jin, K.H., McCann, M.T., Froustey, E., Unser, M.: Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process. 26(9), 4509–4522 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  15. Kak, A.C.: Algorithms for reconstruction with nondiffracting sources. In: Principles of Computerized Tomographic Imaging, pp. 49–112 (2001)

    Google Scholar 

  16. Kamruzzaman, M., ElMasry, G., Sun, D.W., Allen, P.: Application of NIR hyperspectral imaging for discrimination of lamb muscles. J. Food Engineer. 104(3), 332–340 (2011)

    Article  Google Scholar 

  17. Kang, E., Min, J., Ye, J.C.: A deep convolutional neural network using directional wavelets for low-dose x-ray CT reconstruction. J. Medical Phys. 44(10), e360–e375 (2017)

    Article  Google Scholar 

  18. Kawase, K., Ogawa, Y., Watanabe, Y., Inoue, H.: Non-destructive terahertz imaging of illicit drugs using spectral fingerprints. Opt. Express 11(20), 2549–2554 (2003)

    Article  Google Scholar 

  19. Mao, X., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Proceedings of Advances in Neural Information Processing Systems, pp. 2802–2810 (2016)

    Google Scholar 

  20. Meyer, C.D.: Matrix Analysis and Applied Linear Algebra. SIAM (2000)

    Google Scholar 

  21. Mittleman, D., Gupta, M., Neelamani, R., Baraniuk, R., Rudd, J., Koch, M.: Recent advances in terahertz imaging. Appl. Phys. B 68(6), 1085–1094 (1999)

    Article  Google Scholar 

  22. Mittleman, D.M.: Twenty years of terahertz imaging. Opt. Express 26(8), 9417–9431 (2018)

    Article  Google Scholar 

  23. Ozdemir, A., Polat, K.: Deep learning applications for hyperspectral imaging: a systematic review. J. Inst. Electron. Comput. 2(1), 39–56 (2020)

    Article  Google Scholar 

  24. Popescu, D.C., Ellicar, A.D.: Point spread function estimation for a terahertz imaging system. EURASIP J. Adv. Sig. Process. 2010(1), 575817 (2010)

    Article  Google Scholar 

  25. Popescu, D.C., Hellicar, A., Li, Y.: Phantom-based point spread function estimation for terahertz imaging system. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2009. LNCS, vol. 5807, pp. 629–639. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04697-1_59

    Chapter  Google Scholar 

  26. Qin, X., Wang, X., Bai, Y., Xie, X., Jia, H.: FFA-net: feature fusion attention network for single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 11908–11915 (2020)

    Google Scholar 

  27. Recur, B., et al.: Investigation on reconstruction methods applied to 3D terahertz computed tomography. Opt. Express 19(6), 5105–5117 (2011)

    Article  Google Scholar 

  28. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Proceedings of Interenaional Conference on Medical Image Computer and Computing.-Assisted Intervention, pp. 234–241 (2015)

    Google Scholar 

  29. Rotermund, H.H., Engel, W., Jakubith, S., Von Oertzen, A., Ertl, G.: Methods and application of UV photoelectron microscopy in heterogenous catalysis. Ultramicroscopy 36(1–3), 164–172 (1991)

    Article  Google Scholar 

  30. Round, A.R., et al.: A preliminary study of breast cancer diagnosis using laboratory based small angle x-ray scattering. Phys. Med. Biol. 50(17), 4159 (2005)

    Article  Google Scholar 

  31. Saeedkia, D.: Handbook of Terahertz Technology for Imaging, Sensing and Communications. Elsevier, Amsterdam (2013)

    Google Scholar 

  32. Schultz, R., Nielsen, T., Zavaleta, R.J., Wyatt, R., Garner, H.: Hyperspectral imaging: a novel approach for microscopic analysis. Cytometry 43(4), 239–247 (2001)

    Article  Google Scholar 

  33. Slocum, D.M., Slingerland, E.J., Giles, R.H., Goyette, T.M.: Atmospheric absorption of terahertz radiation and water vapor continuum effects. J. Quant. Spectrosc. Radiat. Transf. 127, 49–63 (2013)

    Article  Google Scholar 

  34. Spies, J.A., et al.: Terahertz spectroscopy of emerging materials. J. Phys. Chem. C 124(41), 22335–22346 (2020)

    Article  Google Scholar 

  35. Tuan, T.M., Fujita, H., Dey, N., Ashour, A.S., Ngoc, T.N., Chu, D.T., et al.: Dental diagnosis from x-ray images: an expert system based on fuzzy computing. Biomed. Sig. Process. Control 39, 64–73 (2018)

    Article  Google Scholar 

  36. Van Exter, M., Fattinger, C., Grischkowsky, D.: Terahertz time-domain spectroscopy of water vapor. Opt. Lett. 14(20), 1128–1130 (1989)

    Article  Google Scholar 

  37. Wong, T.M., Kahl, M., Bolívar, P.H., Kolb, A.: Computational image enhancement for frequency modulated continuous wave (FMCW) THZ image. J. Infrared Millimeter Terahertz Waves 40(7), 775–800 (2019)

    Google Scholar 

  38. Xie, X.: A review of recent advances in surface defect detection using texture analysis techniques. ELCVIA: Electron. Lett. Comput. Vis. Iimage Ana. 1–22 (2008)

    Google Scholar 

  39. Yujiri, L., Shoucri, M., Moffa, P.: Passive millimeter wave imaging. IEEE Microwave Mag. 4(3), 39–50 (2003)

    Article  Google Scholar 

  40. Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. In: Proceedings of International Conference on Machine learning, pp. 7354–7363 (2019)

    Google Scholar 

  41. Zhang, K., Ana Y. Chen, W.Z., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)

    Google Scholar 

  42. Zhang, K., Zuo, W.M., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608–4622 (2018)

    Article  MathSciNet  Google Scholar 

  43. Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. (2020)

    Google Scholar 

  44. Zhu, B., Liu, J.Z., Cauley, S.F., Rosen, R.B., Rosen, M.S.: Image reconstruction by domain-transform manifold learning. Nature 555(7697), 487–492 (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of EE, National Tsing Hua University, Hsinchu, 300044, Taiwan

    Wen-Tai Su, Po-Jen Yu, Shang-Hua Yang & Chia-Wen Lin

  2. Department of ECE, University of California, Los Angeles, USA

    Yi-Chun Hung

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  1. Wen-Tai Su
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  2. Yi-Chun Hung
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  3. Po-Jen Yu
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  4. Shang-Hua Yang
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  5. Chia-Wen Lin
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Corresponding author

Correspondence to Chia-Wen Lin .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Su, WT., Hung, YC., Yu, PJ., Yang, SH., Lin, CW. (2022). Seeing Through a Black Box: Toward High-Quality Terahertz Imaging via Subspace-and-Attention Guided Restoration. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13667. Springer, Cham. https://doi.org/10.1007/978-3-031-20071-7_27

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  • DOI: https://doi.org/10.1007/978-3-031-20071-7_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20070-0

  • Online ISBN: 978-3-031-20071-7

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