{"id":"https://openalex.org/W7164539291","doi":"https://doi.org/10.1109/tci.2026.3703214","title":"Deep Learning Approach for Microwave Imaging Based on Deep Convolutional Asymmetric Encoder-Decoder Structure and Physics-Induced Loss","display_name":"Deep Learning Approach for Microwave Imaging Based on Deep Convolutional Asymmetric Encoder-Decoder Structure and Physics-Induced Loss","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7164539291","doi":"https://doi.org/10.1109/tci.2026.3703214"},"language":null,"primary_location":{"id":"doi:10.1109/tci.2026.3703214","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tci.2026.3703214","pdf_url":null,"source":{"id":"https://openalex.org/S4210233665","display_name":"IEEE Transactions on Computational Imaging","issn_l":"2333-9403","issn":["2333-9403","2334-0118","2573-0436"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112410365","display_name":"He Ming Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"He Ming Yao","raw_affiliation_strings":["Department of Automation and BNRist, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2814-9539","affiliations":[{"raw_affiliation_string":"Department of Automation and BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101868179","display_name":"Shiji Song","orcid":"https://orcid.org/0000-0003-0858-1770"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiji Song","raw_affiliation_strings":["Department of Automation and BNRist, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7361-9283","affiliations":[{"raw_affiliation_string":"Department of Automation and BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113605545","display_name":"K. Ng","orcid":null},"institutions":[{"id":"https://openalex.org/I141568987","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131","country_code":"HK","type":"education","lineage":["https://openalex.org/I141568987"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Michael Kwok Po NG","raw_affiliation_strings":["Department of Mathematics, Hong Kong Baptist University, Hong Kong SAR, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Hong Kong Baptist University, Hong Kong SAR, China","institution_ids":["https://openalex.org/I141568987"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112394930","display_name":"Lijun Jiang","orcid":"https://orcid.org/0000-0002-7391-6322"},"institutions":[{"id":"https://openalex.org/I20382870","display_name":"Missouri University of Science and Technology","ror":"https://ror.org/00scwqd12","country_code":"US","type":"education","lineage":["https://openalex.org/I20382870"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lijun Jiang","raw_affiliation_strings":["Missouri University of Science and Technology, Rolla, MO, USA"],"raw_orcid":"https://orcid.org/0000-0002-7391-6322","affiliations":[{"raw_affiliation_string":"Missouri University of Science and Technology, Rolla, MO, USA","institution_ids":["https://openalex.org/I20382870"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.75376637,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"1194","last_page":"1204"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.26109999418258667,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.26109999418258667,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.14249999821186066,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11607","display_name":"Microwave and Dielectric Measurement Techniques","score":0.08299999684095383,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7791000008583069},{"id":"https://openalex.org/keywords/microwave-imaging","display_name":"Microwave imaging","score":0.6635000109672546},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.534500002861023},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.41350001096725464},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.40290001034736633},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3978999853134155},{"id":"https://openalex.org/keywords/microwave","display_name":"Microwave","score":0.37709999084472656},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.35350000858306885}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7791000008583069},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7764000296592712},{"id":"https://openalex.org/C2779885931","wikidata":"https://www.wikidata.org/wiki/Q17010029","display_name":"Microwave imaging","level":3,"score":0.6635000109672546},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6237999796867371},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5946999788284302},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.534500002861023},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.41350001096725464},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.40290001034736633},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3978999853134155},{"id":"https://openalex.org/C44838205","wikidata":"https://www.wikidata.org/wiki/Q127995","display_name":"Microwave","level":2,"score":0.37709999084472656},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.35350000858306885},{"id":"https://openalex.org/C187107819","wikidata":"https://www.wikidata.org/wiki/Q835696","display_name":"NASA Deep Space Network","level":3,"score":0.3377000093460083},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.32339999079704285},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.31700000166893005},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.30090001225471497},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.30070000886917114},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2922999858856201},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C92630104","wikidata":"https://www.wikidata.org/wiki/Q4115103","display_name":"Optical imaging","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2766999900341034},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.2603999972343445},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tci.2026.3703214","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tci.2026.3703214","pdf_url":null,"source":{"id":"https://openalex.org/S4210233665","display_name":"IEEE Transactions on Computational Imaging","issn_l":"2333-9403","issn":["2333-9403","2334-0118","2573-0436"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Imaging","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5313418749","display_name":null,"funder_award_id":"42327901","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1,94],"paper,":[2],"we":[3,63],"introduce":[4],"an":[5,65],"innovative":[6],"deep":[7,27],"learning":[8],"(DL)":[9],"methodology":[10],"designed":[11],"for":[12,128,171],"real-time":[13],"quantitative":[14],"microwave":[15],"imaging":[16],"(MWI).":[17],"Our":[18,162],"approach":[19,164],"is":[20,90],"centered":[21],"around":[22],"the":[23,41,51,55,59,73,79,86,100,115,118,125,129,137,140,146,153,160,192],"utilization":[24],"of":[25,40,54,120,196],"a":[26,36],"convolutional":[28],"asymmetric":[29],"encoder-decoder":[30],"structure":[31],"(DCAEDS),":[32],"which":[33],"requires":[34],"only":[35],"single-frequency":[37],"far-field":[38],"measurement":[39],"electromagnetic":[42],"(EM)":[43],"scattered":[44,75,143,149],"field":[45,76,144,150],"as":[46,181],"input":[47],"and":[48,124,145,184,194],"subsequently":[49],"predicts":[50],"contrasts":[52,82,131,156],"(permittivities)":[53,83,157],"target":[56,81,130,155],"materials.":[57],"During":[58],"offline":[60],"training":[61],"process,":[62],"incorporate":[64],"EM":[66,74,95,142,148],"forward":[67,96],"solver":[68,97],"specifically":[69],"crafted":[70],"to":[71,98],"compute":[72],"generated":[77,158],"by":[78,85,159],"predicted":[80,154],"produced":[84],"DCAEDS.":[87,161],"The":[88],"DCAEDS":[89,123],"seamlessly":[91],"integrated":[92],"with":[93,177],"optimize":[99],"loss":[101,104],"function.":[102],"This":[103],"function":[105],"comprises":[106],"two":[107],"fundamental":[108],"components:":[109],"(1)":[110],"Data-induced":[111],"loss,":[112,135],"directly":[113],"quantifying":[114],"dissimilarity":[116],"between":[117,139],"predictions":[119],"our":[121,197],"proposed":[122],"actual":[126],"labels":[127],"(permittivities);":[132],"(2)":[133],"Physics-induced":[134],"evaluating":[136],"distinctions":[138],"measured":[141],"computed":[147],"derived":[151],"from":[152],"DL":[163],"excels":[165],"in":[166],"delivering":[167],"precise":[168],"results,":[169],"even":[170],"high-contrast":[172],"targets,":[173],"overcoming":[174],"limitations":[175],"associated":[176],"conventional":[178],"methods,":[179],"such":[180],"computational":[182],"cost":[183],"ill-conditioning.":[185],"Numerical":[186],"benchmarks":[187],"using":[188],"dielectric":[189],"targets":[190],"underscore":[191],"practicality":[193],"effectiveness":[195],"DL-based":[198],"approach.":[199]},"counts_by_year":[],"updated_date":"2026-07-10T05:49:55.623906","created_date":"2026-06-13T00:00:00"}