{"id":"https://openalex.org/W4396667093","doi":"https://doi.org/10.1109/lsp.2024.3396670","title":"Contrastive Semi-Supervised Learning for Image Highlight Removal","display_name":"Contrastive Semi-Supervised Learning for Image Highlight Removal","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4396667093","doi":"https://doi.org/10.1109/lsp.2024.3396670"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2024.3396670","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/lsp.2024.3396670","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","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/A5114157346","display_name":"Pengyue Li","orcid":null},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Pengyue Li","raw_affiliation_strings":["College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, China","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114339758","display_name":"Xiaolan Li","orcid":"https://orcid.org/0009-0008-9689-8957"},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolan Li","raw_affiliation_strings":["Library, Taiyuan University of Technology, Taiyuan, China"],"affiliations":[{"raw_affiliation_string":"Library, Taiyuan University of Technology, Taiyuan, China","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100451430","display_name":"Wentao Li","orcid":"https://orcid.org/0009-0000-7201-9868"},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentao Li","raw_affiliation_strings":["College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, China","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088143484","display_name":"Xinying Xu","orcid":"https://orcid.org/0000-0001-5968-5989"},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinying Xu","raw_affiliation_strings":["College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, China","institution_ids":["https://openalex.org/I9086337"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5114157346"],"corresponding_institution_ids":["https://openalex.org/I9086337"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05420014,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"31","issue":null,"first_page":"1334","last_page":"1338"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":1.0,"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/T11019","display_name":"Image Enhancement Techniques","score":1.0,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11666","display_name":"Color Science and Applications","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7823679447174072},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7167360782623291},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6840678453445435},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5797343254089355},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5702965259552002},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.536133348941803},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.52708899974823},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4396277666091919},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43902358412742615},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4306972622871399},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.428060382604599},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42381802201271057},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.41881322860717773},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3469063639640808}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7823679447174072},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7167360782623291},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6840678453445435},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5797343254089355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5702965259552002},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.536133348941803},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.52708899974823},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4396277666091919},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43902358412742615},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4306972622871399},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.428060382604599},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42381802201271057},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.41881322860717773},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3469063639640808},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2024.3396670","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/lsp.2024.3396670","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6210459843","display_name":null,"funder_award_id":"62203319","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8139096407","display_name":null,"funder_award_id":"202103021224056","funder_id":"https://openalex.org/F4320322666","funder_display_name":"Natural Science Foundation of Shanxi Province"},{"id":"https://openalex.org/G8461480591","display_name":null,"funder_award_id":"202203021212220","funder_id":"https://openalex.org/F4320322666","funder_display_name":"Natural Science Foundation of Shanxi Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322666","display_name":"Natural Science Foundation of Shanxi Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1524528038","https://openalex.org/W1953086554","https://openalex.org/W1991135461","https://openalex.org/W2079686637","https://openalex.org/W2146452289","https://openalex.org/W2408719804","https://openalex.org/W2567309586","https://openalex.org/W2590725495","https://openalex.org/W2922509574","https://openalex.org/W2963472846","https://openalex.org/W2964315494","https://openalex.org/W2983829898","https://openalex.org/W2996883610","https://openalex.org/W3004654097","https://openalex.org/W3092667514","https://openalex.org/W3148327892","https://openalex.org/W3171168626","https://openalex.org/W3180940425","https://openalex.org/W3185905868","https://openalex.org/W3210157813","https://openalex.org/W4225672218","https://openalex.org/W4283261295","https://openalex.org/W4286588055","https://openalex.org/W4312430207","https://openalex.org/W4312812783","https://openalex.org/W4315473462","https://openalex.org/W4365479425","https://openalex.org/W4379382445","https://openalex.org/W4386066015","https://openalex.org/W4386076148","https://openalex.org/W4386076264","https://openalex.org/W4386076281","https://openalex.org/W4390871791","https://openalex.org/W4391036470","https://openalex.org/W6733814495","https://openalex.org/W6797227315","https://openalex.org/W6839812757"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W4231274751","https://openalex.org/W1549363203","https://openalex.org/W2154063878","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W1538046993","https://openalex.org/W2571255492","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Image":[0],"highlight":[1,31,50,61],"removal":[2],"is":[3,22],"a":[4,39,83,89],"fundamental":[5],"and":[6,27,55,72,88,99,122,140],"challenging":[7],"visual":[8],"task.":[9],"Although":[10],"fully":[11],"supervised":[12],"deep":[13,42],"learning-based":[14],"methods":[15],"have":[16],"achieved":[17],"remarkable":[18],"results,":[19],"their":[20],"performance":[21],"limited":[23],"by":[24,119],"the":[25,69,76,105,113,120,135],"diversity":[26],"quantity":[28],"of":[29,68,104,115],"paired":[30,54],"images.":[32],"To":[33],"address":[34],"this":[35],"issue,":[36],"we":[37],"propose":[38],"student-teacher":[40],"semi-supervised":[41,65],"learning":[43,48],"method":[44,146],"based":[45],"on":[46,150],"contrastive":[47],"for":[49,58],"removal,":[51],"which":[52],"integrates":[53,82],"unpaired":[56],"data":[57],"boosting":[59],"image":[60],"removal.":[62],"Specifically,":[63],"our":[64,145],"network":[66],"consists":[67],"parallel":[70,90,126],"student":[71],"teacher":[73],"sub-networks":[74],"with":[75,134],"same":[77],"U-shaped":[78],"CTransformer.":[79],"The":[80,107,125],"CTransformer":[81],"dual":[84,108],"multiscale":[85,109],"convolution":[86,110],"module":[87,93,111,129],"multiaxial":[91,127],"self-attention":[92,128],"to":[94],"promote":[95],"local":[96],"feature":[97],"representation":[98,114],"global":[100],"contextual":[101],"semantic":[102],"comprehension":[103],"network.":[106],"realizes":[112],"multiple":[116],"perceptual":[117],"fields":[118],"internal":[121],"external":[123],"multiscales.":[124],"implements":[130],"multidimensional":[131],"autocorrelation":[132],"attention":[133],"selective":[136],"fusion":[137],"mechanism.":[138],"Quantitative":[139],"qualitative":[141],"results":[142,149],"show":[143],"that":[144],"takes":[147],"SOAT":[148],"different":[151],"datasets.":[152]},"counts_by_year":[],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}