{"id":"https://openalex.org/W2771594282","doi":"https://doi.org/10.1109/iccphot.2018.8368474","title":"Learned perceptual image enhancement","display_name":"Learned perceptual image enhancement","publication_year":2018,"publication_date":"2018-05-01","ids":{"openalex":"https://openalex.org/W2771594282","doi":"https://doi.org/10.1109/iccphot.2018.8368474","mag":"2771594282"},"language":"en","primary_location":{"id":"doi:10.1109/iccphot.2018.8368474","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccphot.2018.8368474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Computational Photography (ICCP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1712.02864","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102759044","display_name":"Hossein Talebi","orcid":"https://orcid.org/0000-0002-5962-2563"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hossein Talebi","raw_affiliation_strings":["Google Research, Mountain View, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Research, Mountain View, CA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002085979","display_name":"Peyman Milanfar","orcid":"https://orcid.org/0000-0003-1455-7662"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peyman Milanfar","raw_affiliation_strings":["Google Research, Mountain View, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Research, Mountain View, CA","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1291425158"],"apc_list":null,"apc_paid":null,"fwci":0.1521,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.46677992,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":1.0,"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":1.0,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9990000128746033,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9988999962806702,"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/pipeline","display_name":"Pipeline (software)","score":0.723440945148468},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7043381333351135},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6641562581062317},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6247135400772095},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.623236894607544},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5899286270141602},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5845078825950623},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5460351705551147},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.5428301692008972},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.5248584747314453},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5132929682731628},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3770313858985901},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3491198420524597},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06885823607444763}],"concepts":[{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.723440945148468},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7043381333351135},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6641562581062317},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6247135400772095},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.623236894607544},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5899286270141602},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5845078825950623},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5460351705551147},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5428301692008972},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.5248584747314453},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5132929682731628},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3770313858985901},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3491198420524597},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06885823607444763},{"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/iccphot.2018.8368474","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccphot.2018.8368474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Computational Photography (ICCP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1712.02864","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1712.02864","pdf_url":"https://arxiv.org/pdf/1712.02864","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2771594282","is_oa":true,"landing_page_url":"http://arxiv.org/pdf/1712.02864.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1712.02864","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1712.02864","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1712.02864","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1712.02864","pdf_url":"https://arxiv.org/pdf/1712.02864","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W2771594282.pdf"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W1580389772","https://openalex.org/W1885185971","https://openalex.org/W1896080482","https://openalex.org/W1906770428","https://openalex.org/W1920280450","https://openalex.org/W2078807908","https://openalex.org/W2090571884","https://openalex.org/W2109255472","https://openalex.org/W2126926806","https://openalex.org/W2133665775","https://openalex.org/W2171235719","https://openalex.org/W2183341477","https://openalex.org/W2187446163","https://openalex.org/W2217895792","https://openalex.org/W2293846591","https://openalex.org/W2417716951","https://openalex.org/W2467531333","https://openalex.org/W2515223471","https://openalex.org/W2520715860","https://openalex.org/W2562637781","https://openalex.org/W2604528050","https://openalex.org/W2754213847","https://openalex.org/W2963452532","https://openalex.org/W3138063419","https://openalex.org/W6638667902","https://openalex.org/W6656529242","https://openalex.org/W6678856934","https://openalex.org/W6683590716","https://openalex.org/W6684191040","https://openalex.org/W6696085341","https://openalex.org/W6702130928","https://openalex.org/W6717040974","https://openalex.org/W6719781927","https://openalex.org/W6748197083"],"related_works":["https://openalex.org/W2963289467","https://openalex.org/W3170204166","https://openalex.org/W3155694147","https://openalex.org/W2798581339","https://openalex.org/W3100456687","https://openalex.org/W3187505157","https://openalex.org/W2999531799","https://openalex.org/W3207137934","https://openalex.org/W3012565451","https://openalex.org/W3209728799","https://openalex.org/W2884845700","https://openalex.org/W3137956596","https://openalex.org/W2770902049","https://openalex.org/W3180805579","https://openalex.org/W3112587064","https://openalex.org/W3169447391","https://openalex.org/W2996884129","https://openalex.org/W2963031676","https://openalex.org/W2744404335","https://openalex.org/W2945178666"],"abstract_inverted_index":{"Learning":[0],"a":[1,9,46,65,72,99,141],"typical":[2],"image":[3,49,117],"enhancement":[4,58],"pipeline":[5],"involves":[6],"minimization":[7],"of":[8,116,143],"loss":[10,54,82,109,135],"function":[11],"between":[12],"enhanced":[13],"and":[14,104,120,150],"reference":[15,103],"images.":[16],"While":[17],"L1and":[18],"L2losses":[19],"are":[20],"perhaps":[21],"the":[22,53],"most":[23],"widely":[24],"used":[25,112],"functions":[26],"for":[27,96,139],"this":[28,40,134],"purpose,":[29],"they":[30],"do":[31],"not":[32,122],"necessarily":[33],"lead":[34],"to":[35,52,85,93,98,113],"perceptually":[36],"compelling":[37],"results.":[38],"In":[39],"paper,":[41],"we":[42],"show":[43],"that":[44,133],"adding":[45],"learned":[47],"no-reference":[48],"quality":[50],"metric":[51,61],"can":[55,136],"significantly":[56],"improve":[57],"operators.":[59],"This":[60,81,107],"is":[62,110],"implemented":[63],"using":[64],"CNN":[66],"(convolutional":[67],"neural":[68],"network)":[69],"trained":[70],"on":[71],"large-scale":[73],"dataset":[74],"labelled":[75],"with":[76],"aesthetic":[77],"preferences":[78],"ofhuman":[79],"raters.":[80],"allows":[83],"us":[84],"conveniently":[86],"perform":[87],"back-propagation":[88],"in":[89],"our":[90],"learning":[91],"framework":[92],"simultaneously":[94],"optimize":[95],"similarity":[97],"given":[100],"ground":[101],"truth":[102],"perceptual":[105,108],"quality.":[106],"only":[111],"train":[114],"parameters":[115],"processing":[118],"operators,":[119],"does":[121],"impose":[123],"any":[124],"extra":[125],"complexity":[126],"at":[127],"inference":[128],"time.":[129],"Our":[130],"experiments":[131],"demonstrate":[132],"be":[137],"effective":[138],"tuning":[140],"variety":[142],"operators":[144],"such":[145],"as":[146],"local":[147],"tone":[148],"mapping":[149],"dehazing.":[151]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
