{"id":"https://openalex.org/W3063940745","doi":"https://doi.org/10.1145/3388770.3407428","title":"A Fast and Practical CNN Method for Artful Image Regeneration","display_name":"A Fast and Practical CNN Method for Artful Image Regeneration","publication_year":2020,"publication_date":"2020-08-15","ids":{"openalex":"https://openalex.org/W3063940745","doi":"https://doi.org/10.1145/3388770.3407428","mag":"3063940745"},"language":"en","primary_location":{"id":"doi:10.1145/3388770.3407428","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3388770.3407428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM SIGGRAPH 2020 Posters","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/A5101467562","display_name":"Xiaolin Wu","orcid":"https://orcid.org/0000-0002-0103-5374"},"institutions":[{"id":"https://openalex.org/I98251732","display_name":"McMaster University","ror":"https://ror.org/02fa3aq29","country_code":"CA","type":"education","lineage":["https://openalex.org/I98251732"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Xiaolin Wu","raw_affiliation_strings":["McMaster University"],"affiliations":[{"raw_affiliation_string":"McMaster University","institution_ids":["https://openalex.org/I98251732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045294790","display_name":"Qifan Gao","orcid":"https://orcid.org/0000-0003-2768-3609"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qifan Gao","raw_affiliation_strings":["Shanghai Jiao Tong University"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104324128","display_name":"Zhenhao Li","orcid":"https://orcid.org/0009-0006-0287-9188"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenhao Li","raw_affiliation_strings":["Shanghai Jiao Tong University"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101088098","display_name":"Shenglei Li","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shenglei Li","raw_affiliation_strings":["Shanghai Jiao Tong University"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101467562"],"corresponding_institution_ids":["https://openalex.org/I98251732"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08467769,"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":"2"},"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":0.9998000264167786,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9990000128746033,"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":0.6190254092216492},{"id":"https://openalex.org/keywords/regeneration","display_name":"Regeneration (biology)","score":0.6162364482879639},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.576377809047699},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39662104845046997},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3785353899002075}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6190254092216492},{"id":"https://openalex.org/C171056886","wikidata":"https://www.wikidata.org/wiki/Q193119","display_name":"Regeneration (biology)","level":2,"score":0.6162364482879639},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.576377809047699},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39662104845046997},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3785353899002075},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C95444343","wikidata":"https://www.wikidata.org/wiki/Q7141","display_name":"Cell biology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3388770.3407428","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3388770.3407428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM SIGGRAPH 2020 Posters","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2102166818","https://openalex.org/W2607202125","https://openalex.org/W2735974062","https://openalex.org/W2767269772","https://openalex.org/W2768814045","https://openalex.org/W2779483295","https://openalex.org/W2798844427","https://openalex.org/W2963466723","https://openalex.org/W2963967766","https://openalex.org/W3098418424","https://openalex.org/W3125028070"],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2772917594","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398","https://openalex.org/W2775347418"],"abstract_inverted_index":{"Although":[0],"artists\u2019":[1],"actions":[2],"in":[3,11,38,50,60,78,120],"photo":[4,79],"retouching":[5],"appear":[6],"to":[7,16,31,67,95],"be":[8,36],"highly":[9],"nonlinear":[10],"nature":[12],"and":[13,54,81,106,131,149,159],"very":[14],"difficult":[15],"characterize":[17],"analytically,":[18],"we":[19],"find":[20],"that":[21],"the":[22,51,61,70,76,97,101,107,114,121,138,141,157,160,164],"net":[23],"effects":[24],"of":[25,92,100,124,140,147],"interactively":[26],"editing":[27],"a":[28,32,42,56,83,151],"mundane":[29],"image":[30,86,129],"desired":[33],"appearance":[34],"can":[35],"modeled,":[37],"most":[39],"cases,":[40],"by":[41,55,144],"parametric":[43],"monotonically":[44],"non-decreasing":[45],"global":[46,57],"tone":[47,103],"mapping":[48,104,118],"function":[49,105],"luminance":[52,102],"axis":[53],"affine":[58,108],"transform":[59],"chrominance":[62,109],"plane.":[63],"This":[64],"allows":[65],"us":[66],"greatly":[68],"simplify":[69],"existing":[71],"CNN":[72,126],"methods":[73,127],"for":[74,128],"mimicking":[75],"artists":[77],"retouching,":[80],"design":[82],"new":[84,135],"artful":[85],"regeneration":[87],"network":[88,143],"(AIRNet).":[89],"The":[90,133],"objective":[91],"AIRNet":[93],"is":[94],"learn":[96],"image-dependent":[98],"parameters":[99],"transform,":[110],"rather":[111],"than":[112],"learning":[113],"end-to-end":[115],"pixel":[116],"level":[117],"as":[119,150],"standard":[122],"practice":[123],"current":[125],"restoration":[130],"enhancement.":[132],"proposed":[134],"approach":[136],"reduces":[137],"complexity":[139],"neural":[142],"two":[145],"orders":[146],"magnitude,":[148],"side":[152],"benefit,":[153],"it":[154],"also":[155],"improves":[156],"robustness":[158],"generation":[161],"capability":[162],"at":[163],"inference":[165],"stage.":[166]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
