{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T03:39:33Z","timestamp":1778816373772,"version":"3.51.4"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100020950","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["111-2221-E-007-046-MY3"],"award-info":[{"award-number":["111-2221-E-007-046-MY3"]}],"id":[{"id":"10.13039\/501100020950","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s11263-023-01812-y","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T20:32:12Z","timestamp":1686169932000},"page":"2388-2407","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Making the Invisible Visible: Toward High-Quality Terahertz Tomographic Imaging via Physics-Guided Restoration"],"prefix":"10.1007","volume":"131","author":[{"given":"Weng-Tai","family":"Su","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Chun","family":"Hung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Po-Jen","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shang-Hua","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9097-2318","authenticated-orcid":false,"given":"Chia-Wen","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"issue":"2","key":"1812_CR1","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1007\/s10489-020-01829-7","volume":"51","author":"A Abbas","year":"2021","unstructured":"Abbas, A., Abdelsamea, M., & Gaber, M. M. (2021). Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Applied Intelligence, 51(2), 854\u2013864.","journal-title":"Applied Intelligence"},{"issue":"3","key":"1812_CR2","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s00339-010-5642-z","volume":"100","author":"E Abraham","year":"2010","unstructured":"Abraham, E., Younus, A., Delagnes, T. C., & Mounaix, P. (2010). Non-invasive investigation of art paintings by terahertz imaging. Applied Physics A, 100(3), 585\u2013590.","journal-title":"Applied Physics A"},{"key":"1812_CR3","unstructured":"Born, M., & Wolf, E. (2013). Principles of optics: Electromagnetic theory of propagation, interference and diffraction of light."},{"issue":"2","key":"1812_CR4","doi-asserted-by":"publisher","DOI":"10.1117\/1.JBO.23.2.026004","volume":"23","author":"T Bowman","year":"2018","unstructured":"Bowman, T., Chavez, T., Khan, K., Wu, J., Chakraborty, A., Rajaram, N., Bailey, K., & El-Shenawee, M. (2018). Pulsed terahertz imaging of breast cancer in freshly excised murine tumors. Journal of Biomedical Optics, 23(2), 026004.","journal-title":"Journal of Biomedical Optics"},{"issue":"1","key":"1812_CR5","first-page":"33","volume":"2","author":"Y Calvin","year":"2012","unstructured":"Calvin, Y., Shuting, F., Yiwen, S., & Emma, P.-M. (2012). The potential of terahertz imaging for cancer diagnosis: A review of investigations to date. Quantitative Imaging in Medicine and Surgery, 2(1), 33.","journal-title":"Quantitative Imaging in Medicine and Surgery"},{"key":"1812_CR6","unstructured":"Cao, J., Li, Y., Zhang, K., & Van\u00a0Gool, L. (2021). Video super-resolution transformer. arXiv preprint arXiv:2106.06847."},{"key":"1812_CR7","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. In Proceedings of European conference on computer vision (pp. 213\u2013229). Springer.","DOI":"10.1007\/978-3-030-58452-8_13"},{"issue":"11","key":"1812_CR8","doi-asserted-by":"publisher","first-page":"2015","DOI":"10.1088\/0031-9155\/42\/11\/001","volume":"42","author":"D Chapman","year":"1997","unstructured":"Chapman, D., Homlinson, W., Johnston, R., Washburn, D., Pisano, E., Gm\u00fcr, N., Zhong, Z., Menk, R., Arfelli, F., & Sayers, D. (1997). Diffraction enhanced X-ray imaging. Journal Physics in Medicine & Biology, 42(11), 2015.","journal-title":"Journal Physics in Medicine & Biology"},{"key":"1812_CR9","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., & Gao, W. (2021). Pre-trained image processing transformer. In Proceedings of IEEE\/CVF conference on computer vision and pattern recognition (pp. 12299\u201312310).","DOI":"10.1109\/CVPR46437.2021.01212"},{"key":"1812_CR10","doi-asserted-by":"crossref","unstructured":"Cheng, S., Wang, Y., Huang, H., Liu, D., Fan, H., & Liu, S. (2021). 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\u20134906).","DOI":"10.1109\/CVPR46437.2021.00486"},{"issue":"3","key":"1812_CR11","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/0730-725X(94)00124-L","volume":"13","author":"L Clarke","year":"1995","unstructured":"Clarke, L., Velthuizen, R., Camacho, M., Heine, J., Vaidyanathan, M., Hall, L., Thatcher, R., & Silbiger, M. (1995). MRI segmentation: Methods and applications. Magnetic Resonance Imaging, 13(3), 343\u2013368.","journal-title":"Magnetic Resonance Imaging"},{"issue":"1","key":"1812_CR12","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1088\/0022-3727\/29\/1\/023","volume":"29","author":"P Cloetens","year":"1996","unstructured":"Cloetens, P., Barrett, R., Baruchel, J., Guigay, J.-P., & Schlenker, M. (1996). Phase objects in synchrotron radiation hard X-ray imaging. Journal of Physics D: Applied Physics, 29(1), 133.","journal-title":"Journal of Physics D: Applied Physics"},{"issue":"9406","key":"1812_CR13","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/S0140-6736(04)15433-0","volume":"363","author":"AB de Gonzalez","year":"2004","unstructured":"de Gonzalez, A. B., & Darby, S. (2004). Risk of cancer from diagnostic X-rays: Estimates for the UK and 14 other countries. The Lancet, 363(9406), 345\u2013351.","journal-title":"The Lancet"},{"issue":"7","key":"1812_CR14","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1364\/JOSAA.18.001562","volume":"18","author":"TD Dorney","year":"2001","unstructured":"Dorney, T. D., Baraniuk, R. G., & Mittleman, D. M. (2001). Material parameter estimation with terahertz time-domain spectroscopy. Journal of the Optical Society of America A, 18(7), 1562\u20131571.","journal-title":"Journal of the Optical Society of America A"},{"key":"1812_CR15","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., & Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"issue":"7","key":"1812_CR16","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1063\/1.1292471","volume":"53","author":"R Fitzgerald","year":"2000","unstructured":"Fitzgerald, R. (2000). Phase-sensitive X-ray imaging. Physics Today, 53(7), 23\u201326.","journal-title":"Physics Today"},{"key":"1812_CR17","doi-asserted-by":"crossref","unstructured":"Fukunaga, K. (2016). THz technology applied to cultural heritage in practice","DOI":"10.1007\/978-4-431-55885-9"},{"issue":"2","key":"1812_CR18","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.chemolab.2004.01.023","volume":"72","author":"P Geladi","year":"2004","unstructured":"Geladi, P., Burger, J., & Lestander, T. (2004). Hyperspectral imaging: Calibration problems and solutions. Chemometrics and Intelligent Laboratory Systems, 72(2), 209\u2013217.","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"issue":"13","key":"1812_CR19","doi-asserted-by":"publisher","first-page":"16079","DOI":"10.1364\/OE.22.016079","volume":"22","author":"E Hack","year":"2014","unstructured":"Hack, E., & Zolliker, P. (2014). Terahertz holography for imaging amplitude and phase objects. Optics Express, 22(13), 16079\u201316086.","journal-title":"Optics Express"},{"key":"1812_CR20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In Proceedings of IEEE\/CVF international conference on computer vision (pp. 1026\u20131034).","DOI":"10.1109\/ICCV.2015.123"},{"key":"1812_CR21","doi-asserted-by":"crossref","unstructured":"Hung, Y.-C., & Yang, S.-H. (2019a). Kernel size characterization for deep learning terahertz tomography (pp. 1\u20132).","DOI":"10.1109\/IRMMW-THz.2019.8874362"},{"key":"1812_CR22","doi-asserted-by":"crossref","unstructured":"Hung, Y.-C., & Yang, S.-H. (2019b). Terahertz deep learning computed tomography. In Proceedings of international infrared, millimeter, and terahertz waves (pp. 1\u20132). IEEE.","DOI":"10.1109\/IRMMW-THz.2019.8873944"},{"issue":"11","key":"1812_CR23","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.1364\/OL.30.001405","volume":"30","author":"C Janke","year":"2005","unstructured":"Janke, C., F\u00f6rst, M., Nagel, M., Kurz, H., & Bartels, A. (2005). Asynchronous optical sampling for high-speed characterization of integrated resonant terahertz sensors. Optics Letters, 30(11), 1405\u20131407.","journal-title":"Optics Letters"},{"issue":"19","key":"1812_CR24","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1364\/AO.49.000E48","volume":"49","author":"C Jansen","year":"2010","unstructured":"Jansen, C., Wietzke, S., Peters, O., Scheller, M., Vieweg, N., Salhi, M., Krumbholz, N., J\u00f6rdens, C., Hochrein, T., & Koch, M. (2010). Terahertz imaging: Applications and perspectives. Applied Optics, 49(19), 48\u201357.","journal-title":"Applied Optics"},{"issue":"9","key":"1812_CR25","doi-asserted-by":"publisher","first-page":"4509","DOI":"10.1109\/TIP.2017.2713099","volume":"26","author":"KH Jin","year":"2017","unstructured":"Jin, K. H., McCann, M. T., Froustey, E., & Unser, M. (2017). Deep convolutional neural network for inverse problems in imaging. IEEE Transactions on Image Processing, 26(9), 4509\u20134522.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1812_CR26","doi-asserted-by":"crossref","unstructured":"Kak, A. C. (2001). Algorithms for reconstruction with nondiffracting sources. Principles of Computerized Tomographic Imaging, 49\u2013112.","DOI":"10.1137\/1.9780898719277.ch3"},{"issue":"3","key":"1812_CR27","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.jfoodeng.2010.12.024","volume":"104","author":"M Kamruzzaman","year":"2011","unstructured":"Kamruzzaman, M., ElMasry, G., Sun, D.-W., & Allen, P. (2011). Application of NIR hyperspectral imaging for discrimination of lamb muscles. Journal of Food Engineering, 104(3), 332\u2013340.","journal-title":"Journal of Food Engineering"},{"issue":"10","key":"1812_CR28","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1002\/mp.12344","volume":"44","author":"E Kang","year":"2017","unstructured":"Kang, E., Min, J., & Ye, J. C. (2017). A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Journal of Medical physics, 44(10), 360\u2013375.","journal-title":"Journal of Medical physics"},{"issue":"20","key":"1812_CR29","doi-asserted-by":"publisher","first-page":"2549","DOI":"10.1364\/OE.11.002549","volume":"11","author":"K Kawase","year":"2003","unstructured":"Kawase, K., Ogawa, Y., Watanabe, Y., & Inoue, H. (2003). Non-destructive terahertz imaging of illicit drugs using spectral fingerprints. Optics Express, 11(20), 2549\u20132554.","journal-title":"Optics Express"},{"key":"1812_CR30","doi-asserted-by":"crossref","unstructured":"Kim, J., Lim, H., Ahn, S.C., Lee, S. (2018). RGBD camera based material recognition via surface roughness estimation. In: Proceedings of IEEE Winter Conference Applied Computer Vision (pp. 1963\u20131971).","DOI":"10.1109\/WACV.2018.00217"},{"key":"1812_CR31","doi-asserted-by":"crossref","unstructured":"Li, X., & Jarrahi, M. (2020). A 63-pixel plasmonic photoconductive terahertz focal-plane array. In Proceedings of IEEE\/MTT-S international microwave symposium (IMS) (pp. 91\u201394).","DOI":"10.1109\/IMS30576.2020.9224022"},{"issue":"2","key":"1812_CR32","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1148\/radiol.2017170700","volume":"286","author":"F Liu","year":"2018","unstructured":"Liu, F., Jang, H., Kijowski, R., Bradshaw, T., & McMillan, A. B. (2018). Deep learning MR imaging-based attenuation correction for PET\/MR imaging. Radiology, 286(2), 676\u2013684.","journal-title":"Radiology"},{"key":"1812_CR33","doi-asserted-by":"crossref","unstructured":"Ljubenovic, M., Bazrafkan, S., Beenhouwer, J. D., & Sijbers, J. (2020). CNN-based deblurring of terahertz images (pp. 323\u2013330).","DOI":"10.5220\/0008973103230330"},{"key":"1812_CR34","unstructured":"Mao, X., Shen, C., & Yang, Y.-B. (2016) Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In Proceedings of the advances in neural information processing systems (pp. 2802\u20132810)."},{"key":"1812_CR35","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898719512","volume-title":"Matrix analysis and applied linear algebra","author":"CD Meyer","year":"2000","unstructured":"Meyer, C. D. (2000). Matrix analysis and applied linear algebra. SIAM."},{"issue":"8","key":"1812_CR36","doi-asserted-by":"publisher","first-page":"9417","DOI":"10.1364\/OE.26.009417","volume":"26","author":"DM Mittleman","year":"2018","unstructured":"Mittleman, D. M. (2018). Twenty years of terahertz imaging. Optics Express, 26(8), 9417\u20139431.","journal-title":"Optics Express"},{"issue":"6","key":"1812_CR37","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1007\/s003400050750","volume":"68","author":"D Mittleman","year":"1999","unstructured":"Mittleman, D., Gupta, M., Neelamani, R., Baraniuk, R., Rudd, J., & Koch, M. (1999). Recent advances in terahertz imaging. Applied Physics B, 68(6), 1085\u20131094.","journal-title":"Applied Physics B"},{"issue":"8","key":"1812_CR38","doi-asserted-by":"publisher","first-page":"2530","DOI":"10.1364\/AO.375704","volume":"59","author":"E Nunes-Pereira","year":"2020","unstructured":"Nunes-Pereira, E., Peixoto, H., Teixeira, J., & Santos, J. (2020). Polarization-coded material classification in automotive LIDAR aiming at safer autonomous driving implementations. Applied Optics, 59(8), 2530\u20132540.","journal-title":"Applied Optics"},{"issue":"1","key":"1812_CR39","doi-asserted-by":"publisher","first-page":"39","DOI":"10.33969\/JIEC.2020.21004","volume":"2","author":"A Ozdemir","year":"2020","unstructured":"Ozdemir, A., & Polat, K. (2020). Deep learning applications for hyperspectral imaging: A systematic review. Journal of the Institute of Electronics and Computer, 2(1), 39\u201356.","journal-title":"Journal of the Institute of Electronics and Computer"},{"issue":"1","key":"1812_CR40","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1051\/0004-6361:20000021","volume":"365","author":"J Peterson","year":"2001","unstructured":"Peterson, J., Paerels, F., Kaastra, J., Arnaud, M., Reiprich, T., Fabian, A., Mushotzky, R., Jernigan, J., & Sakelliou, I. (2001). X-ray imaging-spectroscopy of Abell 1835. Journal of Astronomy & Astrophysics, 365(1), 104\u2013109.","journal-title":"Journal of Astronomy & Astrophysics"},{"issue":"1","key":"1812_CR41","doi-asserted-by":"publisher","DOI":"10.1155\/2010\/575817","volume":"2010","author":"DC Popescu","year":"2010","unstructured":"Popescu, D. C., & Ellicar, A. D. (2010). Point spread function estimation for a terahertz imaging system. EURASIP Journal on Advances in Signal Processing, 2010(1), 575817.","journal-title":"EURASIP Journal on Advances in Signal Processing"},{"key":"1812_CR42","doi-asserted-by":"crossref","unstructured":"Popescu, D.C., Hellicar, A., & Li, Y. (2009). Phantom-based point spread function estimation for terahertz imaging system (pp. 629\u2013639).","DOI":"10.1007\/978-3-642-04697-1_59"},{"key":"1812_CR43","doi-asserted-by":"crossref","unstructured":"Qin, X., Wang, X., Bai, Y., Xie, X., & Jia, H. (2020). FFA-Net: Feature fusion attention network for single image dehazing. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, pp. 11908\u201311915).","DOI":"10.1609\/aaai.v34i07.6865"},{"issue":"6","key":"1812_CR44","doi-asserted-by":"publisher","first-page":"5817","DOI":"10.1364\/OE.20.005817","volume":"20","author":"B Recur","year":"2012","unstructured":"Recur, B., Guillet, J.-P., Manek-H\u00f6nninger, I., Delagnes, J.-C., Benharbone, W., Desbarats, P., Domenger, J.-P., Canioni, L., & Mounaix, P. (2012). Propagation beam consideration for 3D THz computed tomography. Optics Express, 20(6), 5817\u20135829.","journal-title":"Optics Express"},{"issue":"6","key":"1812_CR45","doi-asserted-by":"publisher","first-page":"5105","DOI":"10.1364\/OE.19.005105","volume":"19","author":"B Recur","year":"2011","unstructured":"Recur, B., Younus, A., Salort, S., Mounaix, P., Chassagne, B., Desbarats, P., Caumes, J., & Abraham, E. (2011). Investigation on reconstruction methods applied to 3D terahertz computed tomography. Optics Express, 19(6), 5105\u20135117.","journal-title":"Optics Express"},{"key":"1812_CR46","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of international conference on medical image computing and computer-assisted intervention (pp. 234\u2013241).","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"1\u20133","key":"1812_CR47","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/0304-3991(91)90148-Y","volume":"36","author":"HH Rotermund","year":"1991","unstructured":"Rotermund, H. H., Engel, W., Jakubith, S., Von Oertzen, A., & Ertl, G. (1991). Methods and application of UV photoelectron microscopy in heterogenous catalysis. Ultramicroscopy, 36(1\u20133), 164\u2013172.","journal-title":"Ultramicroscopy"},{"issue":"17","key":"1812_CR48","doi-asserted-by":"publisher","first-page":"4159","DOI":"10.1088\/0031-9155\/50\/17\/017","volume":"50","author":"AR Round","year":"2005","unstructured":"Round, A. R., Wilkinson, S. J., Hall, C. J., Rogers, K. D., Glatter, O., Wess, T., & Ellis, I. O. (2005). A preliminary study of breast cancer diagnosis using laboratory based small angle x-ray scattering. Physics in Medicine & Biology, 50(17), 4159.","journal-title":"Physics in Medicine & Biology"},{"key":"1812_CR49","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1533\/9780857096494","volume-title":"Handbook of terahertz technology for imaging, sensing and communications","author":"D Saeedkia","year":"2013","unstructured":"Saeedkia, D. (2013). Handbook of terahertz technology for imaging, sensing and communications (pp. 542\u2013578). Cambridge: Woodhead Publishing."},{"issue":"12","key":"1812_CR50","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1038\/nphoton.2010.267","volume":"4","author":"A Sakdinawat","year":"2010","unstructured":"Sakdinawat, A., & Attwood, D. (2010). Nanoscale X-ray imaging. Nature Photonics, 4(12), 840.","journal-title":"Nature Photonics"},{"issue":"4","key":"1812_CR51","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1002\/1097-0320(20010401)43:4<239::AID-CYTO1056>3.0.CO;2-Z","volume":"43","author":"R Schultz","year":"2001","unstructured":"Schultz, R., Nielsen, T., Zavaleta, R. J., Wyatt, R., & Garner, H. (2001). Hyperspectral imaging: A novel approach for microscopic analysis. Cytometry, 43(4), 239\u2013247.","journal-title":"Cytometry"},{"issue":"2","key":"1812_CR52","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/MSP.2022.3198807","volume":"40","author":"W-T Su","year":"2023","unstructured":"Su, W.-T., Hung, Y.-C., Yu, P.-J., Lin, C.-W., & Yang, S.-H. (2023). Physics-guided terahertz computational imaging: A tutorial on sate-of-the-art techniques. IEEE Signal Processing Magazine, 40(2), 32\u201345.","journal-title":"IEEE Signal Processing Magazine"},{"key":"1812_CR53","doi-asserted-by":"crossref","unstructured":"Su, W.-T., Hung, Y.-C., Yu, P.-J., Yang, S.-H., & Lin, C.-W. (2022). Seeing through a black box: Toward high-quality terahertz tomographic imaging via multi-scale spatio-spectral image fusion. In Proceedings of the European conference on computer vision.","DOI":"10.1007\/978-3-031-20071-7_27"},{"key":"1812_CR54","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.bspc.2017.07.005","volume":"39","author":"TM Tuan","year":"2018","unstructured":"Tuan, T. M., Fujita, H., Dey, N., Ashour, A. S., Ngoc, T. N., & Chu, D.-T. (2018). Dental diagnosis from X-ray images: An expert system based on fuzzy computing. Biomedical Signal Processing and Control, 39, 64\u201373.","journal-title":"Biomedical Signal Processing and Control"},{"key":"1812_CR55","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., & Polosukhin, I. (2017) Attention is all you need. In Proceedings of advances in neural information processing systems (vol. 30)."},{"key":"1812_CR56","doi-asserted-by":"crossref","unstructured":"Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., & Li, H. (2022). Uformer: A general U-shaped transformer for image restoration. In Proceedings of IEEE\/CVF conference on computer vision and pattern recognition (pp. 17683\u201317693).","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"1812_CR57","doi-asserted-by":"crossref","unstructured":"Wong, T. M., Kahl, M., & Bol\u00edvar, P.H., Kolb, A. (2019). Computational image enhancement for frequency modulated continuous wave (FMCW) THz image. Journal of Infrared, Millimeter, and Terahertz Waves, 40(7), 775\u2013800.","DOI":"10.1007\/s10762-019-00609-w"},{"key":"1812_CR58","doi-asserted-by":"crossref","unstructured":"Wong, T. M., Kahl, M., Haring-Bol\u00edvar, P., Kolb, A., & M\u00f6ller, M. (2019). Training auto-encoder-based optimizers for terahertz image reconstruction (pp. 93\u2013106).","DOI":"10.1007\/978-3-030-33676-9_7"},{"key":"1812_CR59","unstructured":"Wu, B., Xu, C., Dai, X., Wan, A., Zhang, P., Yan, Z., Tomizuka, M., Gonzalez, J., Keutzer, K., & Vajda, P. (2020). Visual transformers: Token-based image representation and processing for computer vision. arXiv preprint arXiv:2006.03677."},{"issue":"12","key":"1812_CR60","doi-asserted-by":"publisher","first-page":"2919","DOI":"10.1007\/s11263-020-01347-6","volume":"128","author":"H Xie","year":"2020","unstructured":"Xie, H., Yao, H., Zhang, S. P., Zhou, S. C., & Sun, W. X. (2020). Pix2Vox++: Multi-scale context-aware 3D object reconstruction from single and multiple images. International Journal of Computer Vision, 128(12), 2919\u20132935.","journal-title":"International Journal of Computer Vision"},{"key":"1812_CR61","doi-asserted-by":"crossref","unstructured":"Xie, X. (2008). A review of recent advances in surface defect detection using texture analysis techniques. ELCVIA: Electronic Letters on Computer Vision and Image Analysis, 1\u201322","DOI":"10.5565\/rev\/elcvia.268"},{"issue":"3","key":"1812_CR62","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1109\/MMW.2003.1237476","volume":"4","author":"L Yujiri","year":"2003","unstructured":"Yujiri, L., Shoucri, M., & Moffa, P. (2003). Passive millimeter wave imaging. IEEE Microwave Magazine, 4(3), 39\u201350.","journal-title":"IEEE Microwave Magazine"},{"key":"1812_CR63","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2019). Self-attention generative adversarial networks. In Proceedings of international conference on machine learning (pp. 7354\u20137363)."},{"issue":"7","key":"1812_CR64","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, 26(7), 3142\u20133155.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"9","key":"1812_CR65","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang, K., Zuo, W. M., & Zhang, L. (2018). FFDNet: Toward a fast and flexible solution for CNN-based image denoising. IEEE Transactions on Image Processing, 27(9), 4608\u20134622.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1812_CR66","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., & Fu, Y. (2020). Residual dense network for image restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence.","DOI":"10.1109\/TPAMI.2020.2968521"},{"key":"1812_CR67","doi-asserted-by":"crossref","unstructured":"Zhou, S., Zhang, J., Pan, J., Xie, H., Zuo, W., & Ren, J. (2019). Spatio-temporal filter adaptive network for video deblurring. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 2482\u20132491).","DOI":"10.1109\/ICCV.2019.00257"},{"issue":"7697","key":"1812_CR68","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1038\/nature25988","volume":"555","author":"B Zhu","year":"2018","unstructured":"Zhu, B., Liu, J. Z., Cauley, S. F., Rosen, R. B., & Rosen, M. S. (2018). Image reconstruction by domain-transform manifold learning. Nature, 555(7697), 487\u2013492.","journal-title":"Nature"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01812-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-023-01812-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01812-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T16:24:25Z","timestamp":1744215865000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-023-01812-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,7]]},"references-count":68,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["1812"],"URL":"https:\/\/doi.org\/10.1007\/s11263-023-01812-y","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,7]]},"assertion":[{"value":"1 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}