{"id":"https://openalex.org/W7147367393","doi":"https://doi.org/10.1109/icvisp68610.2025.11451709","title":"FreqHDR: Frequency-Enhanced HDR Video Reconstruction","display_name":"FreqHDR: Frequency-Enhanced HDR Video Reconstruction","publication_year":2025,"publication_date":"2025-11-28","ids":{"openalex":"https://openalex.org/W7147367393","doi":"https://doi.org/10.1109/icvisp68610.2025.11451709"},"language":null,"primary_location":{"id":"doi:10.1109/icvisp68610.2025.11451709","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icvisp68610.2025.11451709","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 9th International Conference on Vision, Image and Signal Processing (ICVISP)","raw_type":"proceedings-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/A5090768947","display_name":"Tianxin Huang","orcid":"https://orcid.org/0000-0003-3579-371X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianxin Huang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101550975","display_name":"Yun Zhou","orcid":"https://orcid.org/0000-0002-7466-4593"},"institutions":[{"id":"https://openalex.org/I4210111085","display_name":"Academy of Broadcasting Science","ror":"https://ror.org/01z4nez64","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210111085"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Zhou","raw_affiliation_strings":["NRTA Academy of Broadcasting Science,Beijing,China"],"affiliations":[{"raw_affiliation_string":"NRTA Academy of Broadcasting Science,Beijing,China","institution_ids":["https://openalex.org/I4210111085"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057531458","display_name":"Tian He","orcid":"https://orcid.org/0000-0003-4824-1703"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tian He","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101997106","display_name":"Zhixin Zheng","orcid":"https://orcid.org/0000-0002-9008-2657"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhixin Zheng","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029278873","display_name":"Aidong Men","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aidong Men","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5132636333","display_name":"Zhuqing Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuqing Jiang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Artificial Intelligence Beijing Key Laboratory of Network System and School of Artificial Intelligence,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Artificial Intelligence Beijing Key Laboratory of Network System and School of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5090768947"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.74932207,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9168000221252441,"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":0.9168000221252441,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.026000000536441803,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.014299999922513962,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/ghosting","display_name":"Ghosting","score":0.859000027179718},{"id":"https://openalex.org/keywords/high-dynamic-range","display_name":"High dynamic range","score":0.718500018119812},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6036999821662903},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5241000056266785},{"id":"https://openalex.org/keywords/artifact","display_name":"Artifact (error)","score":0.4975000023841858},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.4803999960422516},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.444599986076355},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43160000443458557},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4311999976634979},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.43059998750686646}],"concepts":[{"id":"https://openalex.org/C2780531524","wikidata":"https://www.wikidata.org/wiki/Q551540","display_name":"Ghosting","level":2,"score":0.859000027179718},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7605999708175659},{"id":"https://openalex.org/C2780056265","wikidata":"https://www.wikidata.org/wiki/Q106239881","display_name":"High dynamic range","level":3,"score":0.718500018119812},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6700999736785889},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.628600001335144},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6036999821662903},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5241000056266785},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.4975000023841858},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.4803999960422516},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.444599986076355},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4311999976634979},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42489999532699585},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4020000100135803},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3961000144481659},{"id":"https://openalex.org/C2781399445","wikidata":"https://www.wikidata.org/wiki/Q309254","display_name":"High-dynamic-range imaging","level":4,"score":0.3937000036239624},{"id":"https://openalex.org/C87133666","wikidata":"https://www.wikidata.org/wiki/Q1161699","display_name":"Dynamic range","level":2,"score":0.39340001344680786},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.38100001215934753},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.34040001034736633},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28139999508857727},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26330000162124634},{"id":"https://openalex.org/C198386975","wikidata":"https://www.wikidata.org/wiki/Q117785","display_name":"Finite impulse response","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.2533000111579895},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.2522999942302704},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.25049999356269836},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icvisp68610.2025.11451709","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icvisp68610.2025.11451709","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 9th International Conference on Vision, Image and Signal Processing (ICVISP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2007207888","https://openalex.org/W2010316810","https://openalex.org/W2738652023","https://openalex.org/W2769654144","https://openalex.org/W2986695366","https://openalex.org/W3139937682","https://openalex.org/W3142504410","https://openalex.org/W3180123900","https://openalex.org/W4386071607","https://openalex.org/W4386076639","https://openalex.org/W4386083028","https://openalex.org/W4390691424","https://openalex.org/W4390874463","https://openalex.org/W4396817377","https://openalex.org/W4401164225","https://openalex.org/W4401329498","https://openalex.org/W4402726955","https://openalex.org/W4402753727","https://openalex.org/W4402753828","https://openalex.org/W4402776179"],"related_works":[],"abstract_inverted_index":{"Reconstructing":[0],"high":[1],"dynamic":[2,25],"range":[3,26],"(HDR)":[4],"video":[5],"from":[6,126],"alternating":[7],"exposure":[8,62],"sequences":[9],"remains":[10],"highly":[11],"challenging.":[12],"Existing":[13],"methods":[14],"primarily":[15],"rely":[16],"on":[17,159],"optical":[18,35],"flow":[19,36],"or":[20,152],"attention":[21,108],"mechanisms":[22],"for":[23,41,82,121],"low":[24],"(LDR)":[27],"sequence":[28],"alignment":[29],"and":[30,52,106,132],"ghosting":[31],"artifact":[32],"suppression.":[33],"However,":[34],"integration":[37],"alone":[38],"proves":[39],"insufficient":[40],"consistent":[42],"performance":[43,154],"improvement":[44],"in":[45],"dynamic/static":[46],"scenes,":[47],"exhibiting":[48],"limited":[49],"noise":[50],"suppression":[51],"suboptimal":[53],"detail":[54],"recovery.":[55],"To":[56],"address":[57],"these":[58],"limitations,":[59],"a":[60,69,78,98,135],"novel":[61],"fusion":[63],"framework,":[64],"FreqHDR,":[65],"is":[66],"proposed,":[67],"incorporating":[68],"dedicated":[70],"Spatial-Frequency":[71],"Integration":[72],"Block":[73],"(SFIB).":[74],"The":[75,95],"encoder":[76],"adopts":[77],"stage-wise":[79],"cascaded":[80],"architecture":[81],"progressive":[83],"multi-scale":[84],"feature":[85],"extraction,":[86],"with":[87],"each":[88],"stage":[89],"enhanced":[90],"by":[91],"the":[92,101,115,147],"SFIB":[93,96],"module.":[94],"follows":[97],"dual-path":[99],"design:":[100],"spatial":[102],"path":[103,117],"employs":[104],"convolutions":[105],"channel":[107],"to":[109,156],"capture":[110],"local":[111],"structural":[112],"details,":[113],"while":[114],"frequency":[116],"utilizes":[118],"Fourier":[119],"transforms":[120],"global":[122],"semantic":[123],"context.":[124],"Features":[125],"both":[127],"domains":[128],"are":[129],"dynamically":[130],"fused":[131],"refined":[133],"via":[134],"gated":[136],"residual":[137],"connection,":[138],"significantly":[139],"strengthening":[140],"representation":[141],"learning.":[142],"Experimental":[143],"results":[144],"demonstrate":[145],"that":[146],"proposed":[148],"method":[149],"achieves":[150],"superior":[151],"competitive":[153],"compared":[155],"state-of-the-art":[157],"approaches":[158],"benchmark":[160],"datasets.":[161]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-04-02T00:00:00"}
