{"id":"https://openalex.org/W7117888262","doi":"https://doi.org/10.1109/access.2025.3650345","title":"Variational Depth Estimation for Endoscopic Images: A Lightweight Unsupervised Learning Approach","display_name":"Variational Depth Estimation for Endoscopic Images: A Lightweight Unsupervised Learning Approach","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7117888262","doi":"https://doi.org/10.1109/access.2025.3650345"},"language":null,"primary_location":{"id":"doi:10.1109/access.2025.3650345","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3650345","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3650345","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121719449","display_name":"Rehman Abdul","orcid":null},"institutions":[{"id":"https://openalex.org/I12832649","display_name":"Gachon University","ror":"https://ror.org/03ryywt80","country_code":"KR","type":"education","lineage":["https://openalex.org/I12832649"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Rehman Abdul","raw_affiliation_strings":["Department of IT Convergence Engineering, Gachon University, Seongnam, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of IT Convergence Engineering, Gachon University, Seongnam, South Korea","institution_ids":["https://openalex.org/I12832649"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121700559","display_name":"Abdul Majeed","orcid":null},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Abdul Majeed","raw_affiliation_strings":["Secure Cyber Systems Research Group, WMG, University of Warwick, Coventry, U.K"],"affiliations":[{"raw_affiliation_string":"Secure Cyber Systems Research Group, WMG, University of Warwick, Coventry, U.K","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107138625","display_name":"Seong Oun Hwang","orcid":null},"institutions":[{"id":"https://openalex.org/I12832649","display_name":"Gachon University","ror":"https://ror.org/03ryywt80","country_code":"KR","type":"education","lineage":["https://openalex.org/I12832649"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seong Oun Hwang","raw_affiliation_strings":["Department of Computer Engineering, Gachon University, Seongnam, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Gachon University, Seongnam, South Korea","institution_ids":["https://openalex.org/I12832649"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5121719449"],"corresponding_institution_ids":["https://openalex.org/I12832649"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00834915,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"2410","last_page":"2419"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.8549000024795532,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10531","display_name":"Advanced Vision and Imaging","score":0.8549000024795532,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.022199999541044235,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.016699999570846558,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/monocular","display_name":"Monocular","score":0.7332000136375427},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7014999985694885},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5680000185966492},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5457000136375427},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4106999933719635},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.37869998812675476},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37560001015663147},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.36169999837875366}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7842000126838684},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7458999752998352},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.7332000136375427},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7014999985694885},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6014000177383423},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5680000185966492},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5457000136375427},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4106999933719635},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.37869998812675476},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37560001015663147},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.36169999837875366},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.2994000017642975},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.28209999203681946},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.28040000796318054},{"id":"https://openalex.org/C109950114","wikidata":"https://www.wikidata.org/wiki/Q4464732","display_name":"3D reconstruction","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.26170000433921814}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2025.3650345","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3650345","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3650345","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3650345","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7917239216","display_name":null,"funder_award_id":"RS-2024-00340882","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2038325351","https://openalex.org/W2067939235","https://openalex.org/W2520707372","https://openalex.org/W2609883120","https://openalex.org/W2776150228","https://openalex.org/W2788765199","https://openalex.org/W2801997348","https://openalex.org/W2900518465","https://openalex.org/W2939157633","https://openalex.org/W2954876976","https://openalex.org/W2962900571","https://openalex.org/W2963163009","https://openalex.org/W2963583471","https://openalex.org/W2982014906","https://openalex.org/W2985775862","https://openalex.org/W2989184872","https://openalex.org/W3152803807","https://openalex.org/W3184350052","https://openalex.org/W4200072305","https://openalex.org/W4285589149","https://openalex.org/W4327989205","https://openalex.org/W4383109207","https://openalex.org/W4390693694","https://openalex.org/W4396909933","https://openalex.org/W4399205886","https://openalex.org/W4400351747","https://openalex.org/W4402727359"],"related_works":[],"abstract_inverted_index":{"Monocular":[0],"depth":[1,33,55,131],"estimation":[2,34,132],"plays":[3],"a":[4,18,77],"critical":[5],"role":[6],"in":[7,44,138],"minimally":[8,139],"invasive":[9,140],"surgery":[10],"(MIS)":[11],"by":[12,121],"enabling":[13,127],"3D":[14,136],"scene":[15],"understanding":[16],"using":[17],"single":[19],"endoscopic":[20,46,68],"image,":[21],"supporting":[22],"compact":[23],"design":[24],"and":[25,41,85,129],"lower":[26],"system":[27],"costs.":[28],"However,":[29],"existing":[30],"unsupervised":[31],"monocular":[32],"(UMDE)":[35],"methods":[36],"struggle":[37],"with":[38,76],"photometric":[39],"inconsistencies":[40],"lack":[42],"robustness":[43],"complex":[45],"environments":[47],"due":[48],"to":[49,82,93,102,124],"the":[50,95,125,145],"absence":[51],"of":[52],"ground":[53],"truth":[54],"data.":[56],"In":[57],"this":[58],"work,":[59],"we":[60,89],"propose":[61],"an":[62,71],"efficient":[63],"UMDE":[64],"framework":[65],"tailored":[66],"for":[67,134],"imaging,":[69],"featuring":[70],"optimized":[72],"MobileNetV2-based":[73],"encoder":[74],"integrated":[75],"First-order":[78],"Variation":[79],"Layer":[80],"(V-layer)":[81],"enhance":[83],"texture":[84],"edge":[86],"representation.":[87],"Additionally,":[88],"incorporate":[90],"structural":[91],"pruning":[92],"compress":[94],"model,":[96],"reducing":[97],"its":[98],"parameters":[99],"from":[100],"5M":[101],"1.25M":[103],"(4.35\u00d7":[104],"compression),":[105],"while":[106],"preserving":[107],"accuracy.":[108],"Experimental":[109],"results":[110],"demonstrate":[111],"that":[112],"our":[113],"method":[114],"reduces":[115],"Absolute":[116],"Relative":[117],"(Abs":[118],"Rel)":[119],"loss":[120],"27.6%":[122],"compared":[123],"baseline,":[126],"accurate":[128],"lightweight":[130],"suitable":[133],"real-time":[135],"reconstruction":[137],"surgery,":[141],"as":[142],"demonstrated":[143],"on":[144],"SCARED":[146],"dataset.":[147]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-01T00:00:00"}