{"id":"https://openalex.org/W4386082972","doi":"https://doi.org/10.23919/mva57639.2023.10216063","title":"Multi-Prior Based Multi-Scale Condition Network for Single-Image HDR Reconstruction","display_name":"Multi-Prior Based Multi-Scale Condition Network for Single-Image HDR Reconstruction","publication_year":2023,"publication_date":"2023-07-23","ids":{"openalex":"https://openalex.org/W4386082972","doi":"https://doi.org/10.23919/mva57639.2023.10216063"},"language":"en","primary_location":{"id":"doi:10.23919/mva57639.2023.10216063","is_oa":false,"landing_page_url":"http://dx.doi.org/10.23919/mva57639.2023.10216063","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 18th International Conference on Machine Vision and Applications (MVA)","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/A5058658513","display_name":"Haorong Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Haorong Jiang","raw_affiliation_strings":["Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135"],"affiliations":[{"raw_affiliation_string":"Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100309782","display_name":"Fengshan Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Fengshan Zhao","raw_affiliation_strings":["Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135"],"affiliations":[{"raw_affiliation_string":"Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010214588","display_name":"Junda Liao","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]},{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN","JP"],"is_corresponding":false,"raw_author_name":"Junda Liao","raw_affiliation_strings":["Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135","Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135","institution_ids":["https://openalex.org/I150744194"]},{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100432519","display_name":"Qin Liu","orcid":"https://orcid.org/0000-0003-3442-0831"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qin Liu","raw_affiliation_strings":["Nanjing University,Nanjing,China","Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University,Nanjing,China","institution_ids":["https://openalex.org/I881766915"]},{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103206427","display_name":"Takeshi Ikenaga","orcid":"https://orcid.org/0000-0001-8338-8175"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takeshi Ikenaga","raw_affiliation_strings":["Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135"],"affiliations":[{"raw_affiliation_string":"Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5058658513"],"corresponding_institution_ids":["https://openalex.org/I150744194"],"apc_list":null,"apc_paid":null,"fwci":0.1228,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.40556947,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"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":0.9998000264167786,"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":0.9998000264167786,"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":0.9995999932289124,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9983999729156494,"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/computer-science","display_name":"Computer science","score":0.7643461227416992},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7222546339035034},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7050015926361084},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6612398028373718},{"id":"https://openalex.org/keywords/high-dynamic-range","display_name":"High dynamic range","score":0.5856319069862366},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5120642781257629},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5081832408905029},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.4812878668308258},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.4726402163505554},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.46169909834861755},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4513782262802124},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42831140756607056},{"id":"https://openalex.org/keywords/dynamic-range","display_name":"Dynamic range","score":0.20518437027931213},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0961000919342041}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7643461227416992},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7222546339035034},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7050015926361084},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6612398028373718},{"id":"https://openalex.org/C2780056265","wikidata":"https://www.wikidata.org/wiki/Q106239881","display_name":"High dynamic range","level":3,"score":0.5856319069862366},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5120642781257629},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5081832408905029},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4812878668308258},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4726402163505554},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.46169909834861755},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4513782262802124},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42831140756607056},{"id":"https://openalex.org/C87133666","wikidata":"https://www.wikidata.org/wiki/Q1161699","display_name":"Dynamic range","level":2,"score":0.20518437027931213},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0961000919342041},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/mva57639.2023.10216063","is_oa":false,"landing_page_url":"http://dx.doi.org/10.23919/mva57639.2023.10216063","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 18th International Conference on Machine Vision and Applications (MVA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7200000286102295}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2476548250","https://openalex.org/W2766497195","https://openalex.org/W2801780873","https://openalex.org/W2963728414","https://openalex.org/W2963814095","https://openalex.org/W2964076515","https://openalex.org/W3006487162","https://openalex.org/W3014641072","https://openalex.org/W3034628923","https://openalex.org/W3122099796","https://openalex.org/W3163431635","https://openalex.org/W3173370405","https://openalex.org/W3173755231","https://openalex.org/W3176234575","https://openalex.org/W3209104673","https://openalex.org/W4285603430","https://openalex.org/W6631190155","https://openalex.org/W6751245404","https://openalex.org/W6761753002"],"related_works":["https://openalex.org/W2580650124","https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3142333283","https://openalex.org/W3122088529","https://openalex.org/W3041320102","https://openalex.org/W2111669074","https://openalex.org/W2085259108","https://openalex.org/W3123087812","https://openalex.org/W2063076820"],"abstract_inverted_index":{"High":[0],"Dynamic":[1],"Range":[2],"(HDR)":[3],"imaging":[4,26],"aims":[5],"to":[6,24,53,75],"reconstruct":[7],"the":[8,16,25,51,86,90,115,122],"natural":[9],"appearance":[10],"of":[11,19,28,82],"real-world":[12],"scenes":[13],"by":[14],"expanding":[15],"bit":[17],"depth":[18],"captured":[20],"images.":[21],"However,":[22],"due":[23],"pipeline":[27],"off-the-shelf":[29],"cameras,":[30],"information":[31,109],"loss":[32],"in":[33,38,56,89,97],"over-exposed":[34,58],"areas":[35,40],"and":[36,60,105],"noise":[37],"under-exposed":[39,62],"pose":[41],"significant":[42],"challenges":[43],"for":[44],"single-image":[45],"HDR":[46,117],"imaging.":[47],"As":[48],"a":[49,67],"result,":[50],"key":[52],"success":[54],"lies":[55],"restoring":[57],"regions":[59],"denoising":[61],"regions.":[63],"In":[64],"this":[65,77],"paper,":[66],"multi-prior":[68],"based":[69],"multi-scale":[70],"condition":[71],"network":[72,92],"is":[73],"proposed":[74,123],"address":[76],"issue.":[78],"(1)":[79],"Three":[80],"types":[81],"prior":[83],"knowledge":[84],"modulate":[85],"intermediate":[87],"features":[88],"reconstruction":[91],"from":[93,110],"different":[94],"perspectives,":[95],"resulting":[96],"improved":[98],"modulation":[99],"effects.":[100],"(2)":[101],"Multi-scale":[102],"fusion":[103],"extracts":[104],"integrates":[106],"deep":[107],"semantic":[108],"various":[111],"priors.":[112],"Experiments":[113],"on":[114],"NTIRE":[116],"challenge":[118],"dataset":[119],"demonstrate":[120],"that":[121],"method":[124],"achieves":[125],"state-of-the-art":[126],"quantitative":[127],"results.":[128]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}