{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:21:22Z","timestamp":1760235682541,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T00:00:00Z","timestamp":1632268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61871386","No.61971427"],"award-info":[{"award-number":["No.61871386","No.61971427"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-quality three-dimensional (3-D) radar imaging is one of the challenging problems in radar imaging enhancement. The existing sparsity regularizations are limited to the heavy computational burden and time-consuming iteration operation. Compared with the conventional sparsity regularizations, the super-resolution (SR) imaging methods based on convolution neural network (CNN) can promote imaging time and achieve more accuracy. However, they are confined to 2-D space and model training under small dataset is not competently considered. To solve these problem, a fast and high-quality 3-D terahertz radar imaging method based on lightweight super-resolution CNN (SR-CNN) is proposed in this paper. First, an original 3-D radar echo model is presented and the expected SR model is derived by the given imaging geometry. Second, the SR imaging method based on lightweight SR-CNN is proposed to improve the image quality and speed up the imaging time. Furthermore, the resolution characteristics among spectrum estimation, sparsity regularization and SR-CNN are analyzed by the point spread function (PSF). Finally, electromagnetic computation simulations are carried out to validate the effectiveness of the proposed method in terms of image quality. The robustness against noise and the stability under small are demonstrate by ablation experiments.<\/jats:p>","DOI":"10.3390\/rs13193800","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T22:50:48Z","timestamp":1632351048000},"page":"3800","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN"],"prefix":"10.3390","volume":"13","author":[{"given":"Lei","family":"Fan","sequence":"first","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Yang","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Qi","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Hongqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Bin","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1109\/36.868873","article-title":"First Demonstration of Airborne SAR Tomography Using Multibaseline L-Band Data","volume":"5","author":"Reigber","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/62.141894","article-title":"SAR looks at planet Earth: On the project of a spacebased three-frequency band synthetic aperture radar (SAR) for exploring natural resources of the Earth and solving ecological problems","volume":"7","author":"Misezhnikov","year":"1992","journal-title":"IEEE Aerosp. Electr. Syst. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/TGRS.2017.2776357","article-title":"SAR Automatic Target Recognition Based on Multiview Deep Learning Framework","volume":"56","author":"Pei","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, Q., Deng, B., Qin, Y., and Wang, H. (2019). Estimation of Translational Motion Parameters in Terahertz Interferometric Inverse Synthetic Aperture Radar (InISAR) Imaging Based on a Strong Scattering Centers Fusion Technique. Remote Sens., 11.","DOI":"10.3390\/rs11101221"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3189","DOI":"10.1109\/TGRS.2011.2178607","article-title":"Three-Dimensional Imaging Using Colocated MIMO Radar and ISAR Technique","volume":"50","author":"Ma","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4296","DOI":"10.1109\/TGRS.2010.2050487","article-title":"Very High Resolution Spaceborne SAR Tomography in Urban Environment","volume":"48","author":"Zhu","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, Q., Deng, B., Qin, Y., and Wang, H. (2019). Experimental Research on Interferometric Inverse Synthetic Aperture Radar Imaging with Multi-Channel Terahertz Radar System. Sensors, 19.","DOI":"10.3390\/s19102330"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhou, S., Li, Y., Zhang, F., Chen, L., and Bu, X. (2019). Automatic Regularization of TomoSAR Point Clouds for Buildings Using Neural Networks. Sensors, 19.","DOI":"10.3390\/s19173748"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2184","DOI":"10.1109\/JSTARS.2016.2549548","article-title":"Superresolution Downward-Looking Linear Array Three-Dimensional SAR Imaging Based on Two-Dimensional Compressive Sensing","volume":"9","author":"Zhang","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e22760","DOI":"10.1002\/mmce.22760","article-title":"Miniaturized microstrip patch antenna with high inter-port isolation for full duplex communication system","volume":"31","author":"Maleki","year":"2021","journal-title":"Int. J. RF Microw. Comput.-Aided Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"179","DOI":"10.2528\/PIERM19100703","article-title":"Mutual Coupling Reduction in Microstrip Array Antenna by Employing Cut Side Patches and EBG Structures","volume":"89","author":"Mohamadzade","year":"2020","journal-title":"Prog. Electromagn. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mohammadi Shirkolaei, M. (2020). Wideband linear microstrip array antenna with high efficiency and low side lobe level. Int. J. RF Microw. Comput.-Aided Eng., 30.","DOI":"10.1002\/mmce.22412"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"234901","DOI":"10.1063\/1.5049204","article-title":"Electromagnetic-wave beam-scanning antenna using near-field rotatable graded-dielectric plates","volume":"124","author":"Afzal","year":"2018","journal-title":"J. Appl. Phys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1080\/09205071.2018.1460280","article-title":"Wideband planar array antenna based on SCRLH-TL for airborne synthetic aperture radar application","volume":"32","author":"Alibakhshikenari","year":"2018","journal-title":"J. Electromagn. Wave"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1916","DOI":"10.1109\/TAP.2019.2891232","article-title":"Single-Dielectric Wideband Partially Reflecting Surface With Variable Reflection Components for Realization of a Compact High-Gain Resonant Cavity Antenna","volume":"67","author":"Lalbakhsh","year":"2019","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lalbakhsh, A., Afzal, M.U., Esselle, K.P., and Smith, S.L. (2018, January 9\u201313). A high-gain wideband ebg resonator antenna for 60 GHz unlicenced frequency band. Proceedings of the 12th European Conference on Antennas and Propagation (EuCAP 2018), London, UK.","DOI":"10.1049\/cp.2018.0998"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1002\/mmce.20942","article-title":"Metamaterial-based antennas for integration in UWB transceivers and portable microwave handsets","volume":"26","year":"2016","journal-title":"Int. J. RF Microw. Comput.-Aided Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"165551","DOI":"10.1016\/j.jmmm.2019.165551","article-title":"A partially ferrite-filled rectangular waveguide with CRLH response and its application to a magnetically scannable antenna","volume":"491","author":"Mohammadi","year":"2019","journal-title":"J. Magn. Magn. Mater."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1186\/s13638-017-0845-z","article-title":"A Doppler aliasing free micro-motion parameter estimation method in the terahertz band","volume":"2017","author":"Yang","year":"2017","journal-title":"J. Wirel. Com. Netw."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, H., Li, C., Wu, S., Zheng, S., and Fang, G. (2021). Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band. Remote Sens., 13.","DOI":"10.3390\/rs13040782"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"045001","DOI":"10.1117\/1.JRS.11.045001","article-title":"Parameter estimation and imaging of rough surface rotating targets in the terahertz band","volume":"11","author":"Yang","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, L., Weng, C., and Li, S. (2021). Passive Remote Sensing of Ice Cloud Properties at Terahertz Wavelengths Based on Genetic Algorithm. Remote Sens., 13.","DOI":"10.3390\/rs13040735"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Y., Hu, W., and Chen, S. (2019). Spatial Resolution Matching of Microwave Radiometer Data with Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11202432"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fan, L., Yang, Q., Zeng, Y., Deng, B., and Wang, H. (2021, January 23\u201326). Multi-View HRRP Recognition Based on Denoising Features Enhancement. Proceedings of the Global Symposium on Millimeter-Waves and Terahertz, Nanjing, China.","DOI":"10.1109\/GSMM53250.2021.9511967"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/TTHZ.2017.2764383","article-title":"Fast Three-Dimensional Image Reconstruction of a Standoff Screening System in the Terahertz Regime","volume":"8","author":"Gao","year":"2018","journal-title":"IEEE Trans. THz Sci. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MSP.2014.2312834","article-title":"Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing","volume":"31","author":"Cetin","year":"2014","journal-title":"IEEE Signal Process. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/JSTSP.2010.2090128","article-title":"Sparse Signal Methods for 3-D Radar Imaging","volume":"5","author":"Austin","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/TSP.2011.2170981","article-title":"Regularized Modified BPDN for Noisy Sparse Reconstruction With Partial Erroneous Support and Signal Value Knowledge","volume":"60","author":"Lu","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4476","DOI":"10.1109\/JSTARS.2020.3014696","article-title":"CSR-Net: A Novel Complex-Valued Network for Fast and Precise 3-D Microwave Sparse Reconstruction","volume":"13","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obser. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4368","DOI":"10.1109\/TSP.2020.3011332","article-title":"Efficient Attributed Scatter Center Extraction Based on Image-Domain Sparse Representation","volume":"68","author":"Yang","year":"2020","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1049\/iet-rsn.2016.0049","article-title":"Three-dimensional super resolution ISAR imaging based on 2D unitary ESPRIT scattering centre extraction technique","volume":"11","author":"Zhao","year":"2017","journal-title":"IET Radar Sonar Navig."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1109\/LGRS.2017.2688461","article-title":"A Fast Patches-Based Imaging Algorithm for 3-D Multistatic Imaging","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yao, L., Qin, C., Chen, Q., and Wu, H. (2021). Automatic Road Marking Extraction and Vectorization from Vehicle-Borne Laser Scanning Data. Remote Sens., 13.","DOI":"10.3390\/rs13132612"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yu, J., Zhou, G., Zhou, S., and Yin, J. (2021). A Lightweight Fully Convolutional Neural Network for SAR Automatic Target Recognition. Remote Sens., 13.","DOI":"10.3390\/rs13153029"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1109\/LGRS.2019.2943069","article-title":"Inverse Synthetic Aperture Radar Imaging Using a Fully Convolutional Neural Network","volume":"17","author":"Hu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Qian, J., Huang, S., Wang, L., Bi, G., and Yang, X. (2021). Super-Resolution ISAR Imaging for Maneuvering Target Based on Deep-Learning-Assisted Time-Frequency Analysis. IEEE Trans. Geosci. Remote Sens., 1\u201314.","DOI":"10.1109\/TGRS.2021.3050189"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/LGRS.2018.2866567","article-title":"Enhanced Radar Imaging Using a Complex-Valued Convolutional Neural Network","volume":"16","author":"Gao","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/LGRS.2020.2965743","article-title":"Enhancing ISAR Resolution by a Generative Adversarial Network","volume":"18","author":"Qin","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhao, D., Jin, T., Dai, Y., Song, Y., and Su, X. (2018). A Three-Dimensional Enhanced Imaging Method on Human Body for Ultra-Wideband Multiple-Input Multiple-Output Radar. Electronics, 7.","DOI":"10.3390\/electronics7070101"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/TCI.2019.2948776","article-title":"Tensor Representation for Three-Dimensional Radar Target Imaging With Sparsely Sampled Data","volume":"6","author":"Qiu","year":"2020","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"10858","DOI":"10.1109\/ACCESS.2021.3050628","article-title":"ATT Squeeze U-Net: A Lightweight Network for Forest Fire Detection and Recognition","volume":"9","author":"Zhang","year":"2021","journal-title":"IEEE Access"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3800\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:03:21Z","timestamp":1760166201000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,22]]},"references-count":41,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193800"],"URL":"https:\/\/doi.org\/10.3390\/rs13193800","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,9,22]]}}}