{"id":"https://openalex.org/W2999380099","doi":"https://doi.org/10.1145/3338533.3368259","title":"Color Recovery from Multi-Spectral NIR Images Using Gray Information","display_name":"Color Recovery from Multi-Spectral NIR Images Using Gray Information","publication_year":2019,"publication_date":"2019-12-15","ids":{"openalex":"https://openalex.org/W2999380099","doi":"https://doi.org/10.1145/3338533.3368259","mag":"2999380099"},"language":"en","primary_location":{"id":"doi:10.1145/3338533.3368259","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3338533.3368259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Multimedia Asia","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/A5048504816","display_name":"Qingtao Fu","orcid":"https://orcid.org/0000-0002-0084-097X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qingtao Fu","raw_affiliation_strings":["Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003623628","display_name":"Cheolkon Jung","orcid":"https://orcid.org/0000-0003-0299-7206"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheolkon Jung","raw_affiliation_strings":["Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045788828","display_name":"Chen Su","orcid":"https://orcid.org/0000-0002-7119-8400"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Su","raw_affiliation_strings":["Huawei Technologies Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Huawei Technologies Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5048504816"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.5356,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.72972817,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9998000264167786,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9991999864578247,"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/T11666","display_name":"Color Science and Applications","score":0.9984999895095825,"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/rgb-color-model","display_name":"RGB color model","score":0.8423128128051758},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.755710244178772},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.652292013168335},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5752594470977783},{"id":"https://openalex.org/keywords/near-infrared-spectroscopy","display_name":"Near-infrared spectroscopy","score":0.5690239667892456},{"id":"https://openalex.org/keywords/color-image","display_name":"Color image","score":0.5105510950088501},{"id":"https://openalex.org/keywords/spectral-color","display_name":"Spectral color","score":0.43198639154434204},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4290424585342407},{"id":"https://openalex.org/keywords/color-filter-array","display_name":"Color filter array","score":0.418935090303421},{"id":"https://openalex.org/keywords/color-model","display_name":"Color model","score":0.33086147904396057},{"id":"https://openalex.org/keywords/color-space","display_name":"Color space","score":0.3289252817630768},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.2357928454875946},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21112820506095886},{"id":"https://openalex.org/keywords/color-gel","display_name":"Color gel","score":0.17104169726371765},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.168574720621109},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.12910109758377075},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07030779123306274}],"concepts":[{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.8423128128051758},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.755710244178772},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.652292013168335},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5752594470977783},{"id":"https://openalex.org/C43571822","wikidata":"https://www.wikidata.org/wiki/Q599037","display_name":"Near-infrared spectroscopy","level":2,"score":0.5690239667892456},{"id":"https://openalex.org/C142616399","wikidata":"https://www.wikidata.org/wiki/Q5148604","display_name":"Color image","level":4,"score":0.5105510950088501},{"id":"https://openalex.org/C97991835","wikidata":"https://www.wikidata.org/wiki/Q910300","display_name":"Spectral color","level":5,"score":0.43198639154434204},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4290424585342407},{"id":"https://openalex.org/C177299597","wikidata":"https://www.wikidata.org/wiki/Q2468214","display_name":"Color filter array","level":5,"score":0.418935090303421},{"id":"https://openalex.org/C36262787","wikidata":"https://www.wikidata.org/wiki/Q2294018","display_name":"Color model","level":4,"score":0.33086147904396057},{"id":"https://openalex.org/C2961294","wikidata":"https://www.wikidata.org/wiki/Q166863","display_name":"Color space","level":3,"score":0.3289252817630768},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2357928454875946},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21112820506095886},{"id":"https://openalex.org/C142771000","wikidata":"https://www.wikidata.org/wiki/Q1435398","display_name":"Color gel","level":4,"score":0.17104169726371765},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.168574720621109},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.12910109758377075},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07030779123306274},{"id":"https://openalex.org/C87359718","wikidata":"https://www.wikidata.org/wiki/Q1271916","display_name":"Thin-film transistor","level":3,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3338533.3368259","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3338533.3368259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Multimedia Asia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2000512760","https://openalex.org/W2033654478","https://openalex.org/W2079811652","https://openalex.org/W2089978268","https://openalex.org/W2092194129","https://openalex.org/W2308529009","https://openalex.org/W2741785014","https://openalex.org/W2791708716","https://openalex.org/W2807758380","https://openalex.org/W2887656403","https://openalex.org/W2963805028","https://openalex.org/W2997449335"],"related_works":["https://openalex.org/W4316813138","https://openalex.org/W3123096349","https://openalex.org/W2970618346","https://openalex.org/W2170952751","https://openalex.org/W1983309380","https://openalex.org/W1982373166","https://openalex.org/W2155393779","https://openalex.org/W2028896475","https://openalex.org/W2729300076","https://openalex.org/W1977788373"],"abstract_inverted_index":{"Converting":[0],"near-infrared":[1],"(NIR)":[2],"images":[3,6,26,56,63,87,146],"into":[4],"color":[5,25,124,135,148,178],"is":[7,82,109,129,172],"a":[8,29,46,67,90,105,112,157],"challenging":[9],"task":[10],"due":[11],"to":[12,49,88,95,100,115,122,142],"the":[13,36,101,117,132,165,169],"different":[14,77],"characteristics":[15],"of":[16,23,76,160,179],"visible":[17],"and":[18,38,140,164],"NIR":[19,31,55,62,73,86,94,103,121,145],"images.":[20],"Most":[21],"methods":[22],"generating":[24],"directly":[27],"from":[28,53,131],"single":[30],"image":[32,108],"are":[33,64],"limited":[34],"by":[35,66,156],"scene":[37,144,162],"object":[39,51],"categories.":[40],"In":[41,98],"this":[42],"paper,":[43],"we":[44],"propose":[45],"novel":[47],"approach":[48,81,152,171],"recovering":[50,177],"colors":[52],"multi-spectral":[54,61,85,102],"using":[57,137],"gray":[58,107],"information.":[59],"The":[60,79,126,150],"obtained":[65,130],"2-CCD":[68],"NIR/RGB":[69],"camera":[70],"with":[71],"narrow":[72],"bandpass":[74],"filters":[75],"wavelengths.":[78],"proposed":[80,151,170],"based":[83],"on":[84],"estimate":[89,116],"conversion":[91,118,127],"matrix":[92,119,128],"for":[93,120,147,176],"RGB":[96,123],"conversion.":[97,125],"addition":[99],"images,":[104,163],"corresponding":[106],"used":[110],"as":[111],"complementary":[113],"channel":[114],"ColorChecker's":[133],"24":[134],"blocks":[136],"polynomial":[138],"regression":[139],"applied":[141],"real-world":[143,161],"recovery.":[149],"has":[153],"been":[154],"evaluated":[155],"large":[158],"number":[159],"results":[166],"show":[167],"that":[168],"simple":[173],"yet":[174],"effective":[175],"objects.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
