{"id":"https://openalex.org/W3177965367","doi":"https://doi.org/10.1109/tip.2021.3096385","title":"Self-Supervised Colorization Towards Monochrome-Color Camera Systems Using Cycle CNN","display_name":"Self-Supervised Colorization Towards Monochrome-Color Camera Systems Using Cycle CNN","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3177965367","doi":"https://doi.org/10.1109/tip.2021.3096385","mag":"3177965367","pmid":"https://pubmed.ncbi.nlm.nih.gov/34270426"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3096385","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE transactions on image processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-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/A5059448769","display_name":"Dong Xuan","orcid":"https://orcid.org/0000-0002-5853-2136"},"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":"Xuan Dong","raw_affiliation_string":"[School of Artificial Intelligence Beijing University of Posts and Telecommunications, Beijing, China]","raw_affiliation_strings":["[School of Artificial Intelligence Beijing University of Posts and Telecommunications, Beijing, China]"]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022256556","display_name":"Chang Liu","orcid":"https://orcid.org/0000-0001-7888-9725"},"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":"Chang Liu","raw_affiliation_string":"[School of Artificial Intelligence Beijing University of Posts and Telecommunications, Beijing, China]","raw_affiliation_strings":["[School of Artificial Intelligence Beijing University of Posts and Telecommunications, Beijing, China]"]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031023511","display_name":"Weixin Li","orcid":"https://orcid.org/0000-0002-7399-8989"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weixin Li","raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China.","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China."]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021725207","display_name":"Xiaoyan Hu","orcid":"https://orcid.org/0000-0002-4312-9889"},"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":"Xiaoyan Hu","raw_affiliation_string":"[School of Artificial Intelligence Beijing University of Posts and Telecommunications, Beijing, China]","raw_affiliation_strings":["[School of Artificial Intelligence Beijing University of Posts and Telecommunications, Beijing, China]"]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021379796","display_name":"Xiaojie Wang","orcid":"https://orcid.org/0000-0001-8402-8999"},"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":"Xiaojie Wang","raw_affiliation_string":"[School of Artificial Intelligence Beijing University of Posts and Telecommunications, Beijing, China]","raw_affiliation_strings":["[School of Artificial Intelligence Beijing University of Posts and Telecommunications, Beijing, China]"]},{"author_position":"last","author":{"id":"https://openalex.org/A5050613147","display_name":"Yunlong Wang","orcid":"https://orcid.org/0000-0002-3535-308X"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhong Wang","raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China.","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China."]}],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"has_fulltext":false,"cited_by_count":6,"cited_by_percentile_year":{"min":86,"max":87},"biblio":{"volume":"30","issue":null,"first_page":"6609","last_page":"6622"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks in Image Processing","score":0.9982,"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/T10775","display_name":"Generative Adversarial Networks in Image Processing","score":0.9982,"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":"Stereo Vision and Depth Estimation","score":0.9979,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9976,"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":[{"keyword":"self-supervised","score":0.25},{"keyword":"monochrome-color","score":0.25}],"concepts":[{"id":"https://openalex.org/C2776754580","wikidata":"https://www.wikidata.org/wiki/Q10770146","display_name":"Monochrome","level":2,"score":0.9298583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.75697947},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.65530455},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5772915},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5220718},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3295828}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3096385","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE transactions on image processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[{"funder":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China","award_id":"61806016"},{"funder":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China","award_id":"61802026"},{"funder":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities","award_id":"2019RC39"}],"referenced_works_count":33,"referenced_works":["https://openalex.org/W1513100184","https://openalex.org/W1986416281","https://openalex.org/W2069281566","https://openalex.org/W2099244020","https://openalex.org/W2099712288","https://openalex.org/W2129112648","https://openalex.org/W2136154655","https://openalex.org/W2326925005","https://openalex.org/W2340897893","https://openalex.org/W2461158874","https://openalex.org/W2462140767","https://openalex.org/W2604231069","https://openalex.org/W2809852002","https://openalex.org/W2885673693","https://openalex.org/W2904798694","https://openalex.org/W2904949811","https://openalex.org/W2922435819","https://openalex.org/W2962793481","https://openalex.org/W2963008638","https://openalex.org/W2963278124","https://openalex.org/W2963502052","https://openalex.org/W2964700958","https://openalex.org/W2979841973","https://openalex.org/W2987285147","https://openalex.org/W2994546229","https://openalex.org/W2997807783","https://openalex.org/W3000407986","https://openalex.org/W3034832575","https://openalex.org/W3034838697","https://openalex.org/W3086079363","https://openalex.org/W3093418165","https://openalex.org/W3100388886","https://openalex.org/W3183537987"],"related_works":["https://openalex.org/W4206962867","https://openalex.org/W2755342338","https://openalex.org/W1891287906","https://openalex.org/W2036807459","https://openalex.org/W2775347418","https://openalex.org/W1969923398","https://openalex.org/W2772917594","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2794975693"],"ngrams_url":"https://api.openalex.org/works/W3177965367/ngrams","abstract_inverted_index":{"Colorization":[0,143],"in":[1,81,394],"monochrome-color":[2,59,131],"camera":[3,18,30,60,132],"systems":[4,61,133],"aims":[5],"to":[6,62,70,146,165,235,291,341,372],"colorize":[7,153,178],"the":[8,16,20,28,43,58,64,71,78,82,86,93,105,111,127,140,148,167,179,195,207,237,254,269,275,287,312,316,330,337,343,369,377,387,391,395,398],"gray":[9,79],"image":[10,22,80,197],"I":[11,154,171,199,211,217,225,320,347,355,403],"G":[14,157,192,202,358,406],"from":[15,27,110,130],"monochrome":[17,34],"using":[19,158,194,390],"color":[21,29,41,50,75,277,400],"R":[23,159,183,189,241,258,279,293,302],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">C":[26,162,174,186,214,220,228,244,261,282,296,305,323,350],"as":[31,163,233,283],"reference.":[32],"Since":[33],"cameras":[35],"have":[36],"better":[37],"imaging":[38],"quality":[39,49],"than":[40],"cameras,":[42],"colorization":[44,149,169,209,239,256,318,378],"can":[45,124,413],"help":[46],"obtain":[47,166,236],"higher":[48],"images.":[51],"Related":[52],"learning":[53],"based":[54],"methods":[55,87,416],"usually":[56],"simulate":[57],"generate":[63],"synthesized":[65,94,106],"data":[66,95,107,129,393],"for":[67,134,253,311,418],"training,":[68],"due":[69],"lack":[72],"of":[73,77,182,198,206,274,315,376,397,402],"ground-truth":[74,284,399],"information":[76,401],"real":[83,102,112,128,392,420],"data.":[84,113,421],"However,":[85],"that":[88,411],"are":[89],"trained":[90],"relying":[91],"on":[92],"may":[96,108],"get":[97],"poor":[98],"results":[99,409],"when":[100],"colorizing":[101,419],"data,":[103],"because":[104],"deviate":[109],"We":[114],"present":[115],"a":[116,364],"self-supervised":[117],"CNN":[118,389],"model,":[119],"named":[120],"Cycle":[121,388],"CNN,":[122],"which":[123],"directly":[125],"use":[126,139,268],"training.":[135],"In":[136,250,360],"detail,":[137],"we":[138,152,177,267,328,362,384,412],"Weighted":[141],"Average":[142],"(WAC)":[144],"network":[145,335,371],"do":[147],"twice.":[150],"First,":[151],"reference":[164,234],"first-time":[168,208,317],"result":[170,210,240,257,319],".":[175,249,309,359,407],"Second,":[176],"de-colored":[180],"map":[181,278],",":[187,193,215,232,266,327],"i.e.":[188,216],"concatenated":[196],"and":[203,224,271,285,336,354],"Cb/Cr":[204],"channels":[205,273],"Cb":[223],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Cr":[231],"second-time":[238,255],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[247,264,299],"'":[248,265],"this":[251],"way,":[252],"Cb":[270],"Cr":[272],"original":[276],"introduce":[286,363],"cycle":[288],"consistency":[289],"loss":[290,340,367],"push":[292],"'Cb/Cr":[300],"\u2248":[301],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Cb/Cr":[308],"Also,":[310],"Y":[313],"channel":[314],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Y":[326,353],"propose":[329],"Global":[331],"Curve":[332],"Adjustment":[333],"(GCA)":[334],"structure":[338,344],"similarity":[339,345],"encourage":[342,373],"between":[346],"addition,":[361],"spatial":[365,374],"smoothness":[366,375],"within":[368],"WAC":[370],"result.":[379],"Combining":[380],"all":[381],"these":[382],"losses,":[383],"could":[385],"train":[386],"absence":[396],"Experimental":[408],"show":[410],"outperform":[414],"related":[415],"largely":[417]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3177965367","counts_by_year":[{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2024-03-25T08:56:05.038479","created_date":"2021-07-19"}