{"id":"https://openalex.org/W2593060940","doi":"https://doi.org/10.1145/3123266.3123370","title":"Learning Non-local Image Diffusion for Image Denoising","display_name":"Learning Non-local Image Diffusion for Image Denoising","publication_year":2017,"publication_date":"2017-10-20","ids":{"openalex":"https://openalex.org/W2593060940","doi":"https://doi.org/10.1145/3123266.3123370","mag":"2593060940"},"language":"en","primary_location":{"id":"doi:10.1145/3123266.3123370","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3123266.3123370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM international conference on Multimedia","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/A5058476148","display_name":"Peng Qiao","orcid":"https://orcid.org/0000-0001-6752-7892"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peng Qiao","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051680867","display_name":"Yong Dou","orcid":"https://orcid.org/0000-0002-1256-8934"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Dou","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084330706","display_name":"Wensen Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wensen Feng","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060842196","display_name":"Rongchun Li","orcid":"https://orcid.org/0000-0001-5922-7961"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rongchun Li","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077938164","display_name":"Yunjin Chen","orcid":"https://orcid.org/0000-0002-4428-2797"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yunjin Chen","raw_affiliation_strings":["ULSee Inc., Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"ULSee Inc., Hangzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5058476148"],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":1.2942,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.87897376,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1847","last_page":"1855"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","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/T10688","display_name":"Image and Signal Denoising Methods","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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9929999709129333,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9889000058174133,"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/noise-reduction","display_name":"Noise reduction","score":0.7724254131317139},{"id":"https://openalex.org/keywords/non-local-means","display_name":"Non-local means","score":0.7378149032592773},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6823155879974365},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6413023471832275},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.588586151599884},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5748621225357056},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.5049486756324768},{"id":"https://openalex.org/keywords/anisotropic-diffusion","display_name":"Anisotropic diffusion","score":0.4607059061527252},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.45241519808769226},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4434775710105896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4321485757827759},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3177213966846466},{"id":"https://openalex.org/keywords/image-denoising","display_name":"Image denoising","score":0.2984544038772583}],"concepts":[{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.7724254131317139},{"id":"https://openalex.org/C101453961","wikidata":"https://www.wikidata.org/wiki/Q7048948","display_name":"Non-local means","level":4,"score":0.7378149032592773},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6823155879974365},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6413023471832275},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.588586151599884},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5748621225357056},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.5049486756324768},{"id":"https://openalex.org/C203504353","wikidata":"https://www.wikidata.org/wiki/Q4765461","display_name":"Anisotropic diffusion","level":3,"score":0.4607059061527252},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.45241519808769226},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4434775710105896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4321485757827759},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3177213966846466},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.2984544038772583}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3123266.3123370","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3123266.3123370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM international conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G24259104","display_name":null,"funder_award_id":"U1435219,61402507,61572515,61402499,61602032","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W658090481","https://openalex.org/W1480546554","https://openalex.org/W1915360731","https://openalex.org/W2006262236","https://openalex.org/W2037981501","https://openalex.org/W2048695508","https://openalex.org/W2049893860","https://openalex.org/W2051434435","https://openalex.org/W2056370875","https://openalex.org/W2077646121","https://openalex.org/W2097073572","https://openalex.org/W2098477387","https://openalex.org/W2099244020","https://openalex.org/W2103559027","https://openalex.org/W2108944658","https://openalex.org/W2109991658","https://openalex.org/W2112796928","https://openalex.org/W2117853853","https://openalex.org/W2130184048","https://openalex.org/W2130975789","https://openalex.org/W2131024476","https://openalex.org/W2131686571","https://openalex.org/W2132680427","https://openalex.org/W2133665775","https://openalex.org/W2140050933","https://openalex.org/W2145263492","https://openalex.org/W2149925139","https://openalex.org/W2150134853","https://openalex.org/W2151452149","https://openalex.org/W2153663612","https://openalex.org/W2160547390","https://openalex.org/W2181908415","https://openalex.org/W2293036172","https://openalex.org/W2488876280","https://openalex.org/W2536599074"],"related_works":["https://openalex.org/W2317217463","https://openalex.org/W2088646524","https://openalex.org/W2761243560","https://openalex.org/W233850645","https://openalex.org/W2475096862","https://openalex.org/W2379232376","https://openalex.org/W2623677214","https://openalex.org/W2776327255","https://openalex.org/W2098237619","https://openalex.org/W2752697732"],"abstract_inverted_index":{"Image":[0],"diffusion":[1,18,41,154],"plays":[2],"a":[3,22,37,142],"fundamental":[4],"role":[5],"for":[6,28,91,157],"the":[7,33,58,68,80,112,116,127,137,162,168,182,203],"task":[8],"of":[9,48,70,144,220],"image":[10,29,89,92,158,215],"denoising.":[11,30,159],"The":[12,177],"recently":[13],"proposed":[14,150],"trainable":[15,151],"nonlinear":[16],"reaction":[17,153],"(TNRD)":[19],"model":[20,35,118,156,185,206],"defines":[21],"simple":[23],"but":[24],"very":[25],"effective":[26,88],"framework":[27],"However,":[31],"as":[32,86],"TNRD":[34,117],"is":[36,43,52,76],"local":[38,49,163,200],"model,":[39],"whose":[40],"behavior":[42],"purely":[44],"controlled":[45],"by":[46,140,173],"information":[47],"patches,":[50],"it":[51,75],"prone":[53],"to":[54,110,119,125,198,212],"create":[55],"artifacts":[56],"in":[57,67,99,218],"homogenous":[59],"regions":[60],"and":[61,147,165,194,225],"over-smooth":[62],"highly":[63,108],"textured":[64],"regions,":[65],"especially":[66],"case":[69],"strong":[71],"noise":[72],"levels.":[73],"Meanwhile,":[74],"widely":[77,97],"known":[78],"that":[79,130,181],"non-local":[81,101,145,152,169],"self-similarity":[82],"(NSS)":[83],"prior":[84,90,114,139],"stands":[85],"an":[87],"denoising,":[93],"which":[94],"has":[95],"been":[96],"exploited":[98],"many":[100],"methods.":[102],"In":[103,123],"this":[104],"work,":[105],"we":[106,135],"are":[107,171],"motivated":[109],"embed":[111],"NSS":[113,138],"into":[115],"tackle":[120],"its":[121,199],"weaknesses.":[122],"order":[124],"preserve":[126],"expected":[128],"property":[129],"end-to-end":[131],"training":[132],"remains":[133],"available,":[134],"exploit":[136],"defining":[141],"set":[143],"filters,":[146],"derive":[148],"our":[149],"(TNLRD)":[155],"Together":[160],"with":[161,191],"filters":[164,170],"influence":[166],"functions,":[167],"learned":[172],"employing":[174],"loss-specific":[175],"training.":[176],"experimental":[178],"results":[179],"show":[180],"trained":[183,204],"TNLRD":[184,205],"produces":[186],"visually":[187],"plausible":[188],"recovered":[189],"images":[190],"more":[192],"textures":[193],"less":[195],"artifacts,":[196],"compared":[197],"versions.":[201],"Moreover,":[202],"can":[207],"achieve":[208],"strongly":[209],"competitive":[210],"performance":[211],"recent":[213],"state-of-the-art":[214],"denoising":[216],"methods":[217],"terms":[219],"peak":[221],"signal-to-noise":[222],"ratio":[223],"(PSNR)":[224],"structural":[226],"similarity":[227],"index":[228],"(SSIM).":[229]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":5}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}
