{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:27:38Z","timestamp":1760232458401,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T00:00:00Z","timestamp":1667952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62201007","U21A20457","62071003","2020M681992","KJ2020A0026","GXXT-2021-028"],"award-info":[{"award-number":["62201007","U21A20457","62071003","2020M681992","KJ2020A0026","GXXT-2021-028"]}]},{"name":"China Postdoctoral Science Foundation","award":["62201007","U21A20457","62071003","2020M681992","KJ2020A0026","GXXT-2021-028"],"award-info":[{"award-number":["62201007","U21A20457","62071003","2020M681992","KJ2020A0026","GXXT-2021-028"]}]},{"name":"Foundation of An\u2019Hui Educational Committee","award":["62201007","U21A20457","62071003","2020M681992","KJ2020A0026","GXXT-2021-028"],"award-info":[{"award-number":["62201007","U21A20457","62071003","2020M681992","KJ2020A0026","GXXT-2021-028"]}]},{"name":"Anhui Province University Collaborative Innovation Project","award":["62201007","U21A20457","62071003","2020M681992","KJ2020A0026","GXXT-2021-028"],"award-info":[{"award-number":["62201007","U21A20457","62071003","2020M681992","KJ2020A0026","GXXT-2021-028"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The millimeter-wave frequency-diverse imaging regime has recently received considerable attention in both the security screening and synthetic aperture radar imaging literature. Considering that the minor systematic errors and alignment errors could still produce heavily corrupted images, these complex-based imaging reconstructions rely heavily on the precise measurement of both phase and amplitude of radiation field patterns and echo signals. In the literature, it is shown that by leveraging phase-retrieval techniques, salient reconstruction images can still be acquired, even in the presence of significant phase errors, which could ease the phase error calibration pressure to a large extent in practical imaging applications. In this paper, in the regime of phaseless frequency-diverse imaging, with the powerful feature inference and generation power of unsupervised generative models, an end-to-end deep prior generative neural network is designed to achieve near real-time imaging. The harsh imaging reconstruction with both the high radiation mode correlations and extremely low scene compression sampling ratio, which are extremely troublesome to tackle for generally applied matched-filter and compressed sensing approach in the current frequency-diverse imaging literature, can still be preferably handled with our reconstruction network. The well-trained reconstruction network is constituted by prior inference and deep generative modules with excellent generative capabilities and significant prior inference abilities. Using simulation experiments with radiation field data, we verify that the integration of phase-free frequency-change imaging with deep learning networks can effectively improve reconstruction capabilities and improve robustness to systematic phase errors. Compared with existing imaging methods, our imaging method has high imaging performance and can even reconstruct targets under low compression ratio conditions, which is somewhat competitive with current state-of-the-art algorithms. Moreover, we find that the proposed method has good anti-noise and stability.<\/jats:p>","DOI":"10.3390\/rs14225665","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T02:07:48Z","timestamp":1668046068000},"page":"5665","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Real-Time Phaseless Microwave Frequency-Diverse Imaging with Deep Prior Generative Neural Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5802-8511","authenticated-orcid":false,"given":"Zhenhua","family":"Wu","sequence":"first","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"},{"name":"East China Research Institute of Electronic Engineering, Hefei 230031, China"},{"name":"State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3820-4119","authenticated-orcid":false,"given":"Fafa","family":"Zhao","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Man","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Qian","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixia","family":"Yang","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1109\/TAP.2020.2968795","article-title":"Review of Metasurface Antennas for Computational Microwave Imaging","volume":"68","author":"Imani","year":"2020","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5096","DOI":"10.1109\/TMTT.2017.2766060","article-title":"GPU-Accelerated Enhanced Resolution 3-D SAR Imaging with Dynamic Metamaterial Antennas","volume":"65","author":"Devadithya","year":"2017","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1126\/science.1230054","article-title":"Metamaterial apertures for computational imaging","volume":"339","author":"Hunt","year":"2013","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1109\/7.18685","article-title":"Geometric accuracy in airborne SAR images","volume":"25","author":"Blacknell","year":"1989","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1109\/TAES.2005.1561888","article-title":"Motion compensation errors: Effects on the accuracy of airborne SAR images","volume":"4","author":"Fornaro","year":"2005","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101","DOI":"10.2528\/PIERB16052801","article-title":"Investigation of Alignment Errors on Multi-Static Microwave Imaging Based on Frequency-Diverse Metamaterial Apertures","volume":"70","author":"Odabasi","year":"2016","journal-title":"Prog. Electromagn. Res. B"},{"key":"ref_7","first-page":"2808","article-title":"Frequency-Diverse Computational Microwave Phaseless Imaging","volume":"16","author":"Yurduseven","year":"2017","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14884","DOI":"10.1109\/ACCESS.2018.2816341","article-title":"Relaxation of Alignment Errors and Phase Calibration in Computational Frequency-Diverse Imaging using Phase Retrieval","volume":"6","author":"Yurduseven","year":"2018","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1109\/TAP.2014.2378262","article-title":"Phaseless synthetic aperture radar with efficient sampling for broadband near-field imaging: Theory and validation","volume":"63","author":"Laviada","year":"2015","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1109\/MAP.2014.6821762","article-title":"Microwave imaging using indirect holographic techniques","volume":"56","author":"Smith","year":"2014","journal-title":"IEEE Antennas Propag. Mag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"011314","DOI":"10.1063\/5.0076022","article-title":"Intelligent meta-imagers: From compressed to learned sensing","volume":"9","author":"Tardif","year":"2022","journal-title":"Appl. Phys. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1109\/LAWP.2014.2386356","article-title":"Reconfgurable array design to realize principal component analysis (pca)-based microwave compressive sensing imaging system","volume":"14","author":"Liang","year":"2015","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1038\/s41467-019-09103-2","article-title":"Machine-learning reprogrammable metasurface imager","volume":"10","author":"Li","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1901913","DOI":"10.1002\/advs.201901913","article-title":"Learned integrated sensing pipeline: Reconfgurable metasurface transceivers as trainable physical layer in an artifcial neural network","volume":"7","author":"Imani","year":"2020","journal-title":"Adv. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"17141","DOI":"10.1038\/lsa.2017.141","article-title":"Phase recovery and holographic image reconstruction using deep learning in neural networks","volume":"7","author":"Rivenson","year":"2018","journal-title":"Light Sci. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"212917","DOI":"10.1109\/ACCESS.2020.3040498","article-title":"Compressive Sensing Radar Imaging with Convolutional Neural Networks","volume":"8","author":"Cheng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gan, F., Yuan, Z., Luo, C., and Wang, H. (2021). Phaseless Terahertz Coded-Aperture Imaging Based on Deep Generative Neural Network. Remote Sens., 13.","DOI":"10.1109\/GSMM53250.2021.9512015"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"6256","DOI":"10.1109\/TAP.2021.3121149","article-title":"Dielectric breast phantoms by generative adversarial network","volume":"70","author":"Shao","year":"2022","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gan, F., Luo, C., Liu, X., Wang, H., and Peng, L. (2020). Fast Terahertz Coded-Aperture Imaging Based on Convolutional Neural Network. Appl. Sci., 10.","DOI":"10.3390\/app10082661"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2259","DOI":"10.1109\/LAWP.2019.2927543","article-title":"Performance Analysis and Dynamic Evolution of Deep Convolutional Neural Network for Electromagnetic Inverse Scattering","volume":"18","author":"Li","year":"2019","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6160","DOI":"10.1109\/TAP.2021.3102032","article-title":"Cascaded Complex U-Net Model to Solve Inverse Scattering Problems With Phaseless-Data in the Complex Domain","volume":"70","author":"Luo","year":"2022","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hyder, R., Shah, V., Hegde, C., and Asif, M. (2019, January 12\u201317). Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval. Proceedings of the ICASSP 2019\u20142019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682811"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2286","DOI":"10.1109\/JSEN.2020.3018751","article-title":"Compressed Sensing-Based Robust Phase Retrieval via Deep Generative Priors","volume":"21","author":"Shamshad","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_26","unstructured":"Kingma, P., and Welling, M. (2014, January 14\u201316). Auto-Encoding Variational Bayes. Proceedings of the 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada."},{"key":"ref_27","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_28","unstructured":"Kingma, D., and Ba, J. (2015, January 7\u20139). Adam:A Method for Stochastic Optimization. Proceedings of the 2015 International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_30","unstructured":"Xiao, H., Rasul, K., and Vollgraf, R. (2017). Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, M., Li, H., Shuang, Y., and Li, L. (2019, January 8\u201311). High-resolution Three-dimensional Microwave Imaging Using a Generative Adversarial Network. Proceedings of the 2019 International Applied Computational Electromagnetics Society Symposium\u2014China (ACES), Nanjing, China.","DOI":"10.23919\/ACES48530.2019.9060477"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5665\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:13:29Z","timestamp":1760145209000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5665"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,9]]},"references-count":31,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14225665"],"URL":"https:\/\/doi.org\/10.3390\/rs14225665","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,11,9]]}}}