{"id":"https://openalex.org/W2971062795","doi":"https://doi.org/10.1109/icip.2019.8804272","title":"Simultaneous Nonlocal Self-Similarity Prior for Image Denoising","display_name":"Simultaneous Nonlocal Self-Similarity Prior for Image Denoising","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2971062795","doi":"https://doi.org/10.1109/icip.2019.8804272","mag":"2971062795"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8804272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8804272","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","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/A5032774989","display_name":"Zhiyuan Zha","orcid":"https://orcid.org/0000-0002-5515-5339"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhiyuan Zha","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015431603","display_name":"Xin Yuan","orcid":"https://orcid.org/0000-0002-8311-7524"},"institutions":[{"id":"https://openalex.org/I72090969","display_name":"Nokia (United States)","ror":"https://ror.org/038km2573","country_code":"US","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I72090969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Yuan","raw_affiliation_strings":["Nokia Bell Labs, Murray Hill, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Nokia Bell Labs, Murray Hill, NJ, USA","institution_ids":["https://openalex.org/I72090969"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024709593","display_name":"Bihan Wen","orcid":"https://orcid.org/0000-0002-6874-6453"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Bihan Wen","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102020945","display_name":"Jiachao Zhang","orcid":"https://orcid.org/0000-0002-3124-9461"},"institutions":[{"id":"https://openalex.org/I2799736854","display_name":"Nanjing Institute of Technology","ror":"https://ror.org/00n6txq60","country_code":"CN","type":"education","lineage":["https://openalex.org/I2799736854"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiachao Zhang","raw_affiliation_strings":["Nanjing Institute of Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing Institute of Technology, Nanjing, China","institution_ids":["https://openalex.org/I2799736854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037979193","display_name":"Jiantao Zhou","orcid":"https://orcid.org/0000-0002-6015-2618"},"institutions":[{"id":"https://openalex.org/I204512498","display_name":"University of Macau","ror":"https://ror.org/01r4q9n85","country_code":"MO","type":"education","lineage":["https://openalex.org/I204512498"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Jiantao Zhou","raw_affiliation_strings":["University of Macau, Macau, China"],"affiliations":[{"raw_affiliation_string":"University of Macau, Macau, China","institution_ids":["https://openalex.org/I204512498"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034427070","display_name":"Ce Zhu","orcid":"https://orcid.org/0000-0001-7607-707X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ce Zhu","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5032774989"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.4049,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.65276979,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1119","last_page":"1123"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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":0.9997000098228455,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9980000257492065,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deblurring","display_name":"Deblurring","score":0.9190671443939209},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6692516803741455},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.6143989562988281},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5695634484291077},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5536792278289795},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5508060455322266},{"id":"https://openalex.org/keywords/feature-detection","display_name":"Feature detection (computer vision)","score":0.5196734070777893},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49683454632759094},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4748956263065338},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.47166693210601807},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.4416431486606598},{"id":"https://openalex.org/keywords/gaussian-blur","display_name":"Gaussian blur","score":0.41319477558135986},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.35707080364227295}],"concepts":[{"id":"https://openalex.org/C2777693668","wikidata":"https://www.wikidata.org/wiki/Q25053743","display_name":"Deblurring","level":5,"score":0.9190671443939209},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6692516803741455},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.6143989562988281},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5695634484291077},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5536792278289795},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5508060455322266},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.5196734070777893},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49683454632759094},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4748956263065338},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.47166693210601807},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4416431486606598},{"id":"https://openalex.org/C104317376","wikidata":"https://www.wikidata.org/wiki/Q1894545","display_name":"Gaussian blur","level":5,"score":0.41319477558135986},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.35707080364227295},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2019.8804272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8804272","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1607704809","https://openalex.org/W1963932623","https://openalex.org/W1969698720","https://openalex.org/W1978749115","https://openalex.org/W1985894000","https://openalex.org/W2011181254","https://openalex.org/W2014311222","https://openalex.org/W2028339660","https://openalex.org/W2045737896","https://openalex.org/W2048695508","https://openalex.org/W2056370875","https://openalex.org/W2075157914","https://openalex.org/W2097073572","https://openalex.org/W2103559027","https://openalex.org/W2107861471","https://openalex.org/W2109240917","https://openalex.org/W2109320267","https://openalex.org/W2113945798","https://openalex.org/W2116857329","https://openalex.org/W2121058967","https://openalex.org/W2125527601","https://openalex.org/W2153663612","https://openalex.org/W2160547390","https://openalex.org/W2172275395","https://openalex.org/W2187214659","https://openalex.org/W2207282238","https://openalex.org/W2288002998","https://openalex.org/W2289219634","https://openalex.org/W2295281957","https://openalex.org/W2505029951","https://openalex.org/W2536599074","https://openalex.org/W2571662414","https://openalex.org/W2606594217","https://openalex.org/W2963495716","https://openalex.org/W3099794995","https://openalex.org/W3100203369","https://openalex.org/W3102870589","https://openalex.org/W6642757213","https://openalex.org/W6662532501","https://openalex.org/W6674723063"],"related_works":["https://openalex.org/W2031788393","https://openalex.org/W2066613488","https://openalex.org/W2279059587","https://openalex.org/W791927757","https://openalex.org/W2315427282","https://openalex.org/W2182590612","https://openalex.org/W3153582293","https://openalex.org/W3207832039","https://openalex.org/W2269775642","https://openalex.org/W3080537281"],"abstract_inverted_index":{"Nonlocal":[0],"image":[1,9,14,16,19,25,40,52,83,95,99,116,134,138,151,196],"representation":[2],"has":[3,46],"achieved":[4],"great":[5],"success":[6],"in":[7,238],"various":[8],"processing":[10],"tasks":[11],"such":[12],"as":[13],"denoising,":[15,117],"deblurring":[17],"and":[18,96,135,143,153,197,241],"deblocking.":[20],"Particularly,":[21],"by":[22,124],"exploiting":[23],"the":[24,38,49,55,65,69,77,89,126,131,177,186,191,194,198,202,216,224],"nonlo-cal":[26],"self-similarity":[27,122],"(NSS)":[28],"prior,":[29],"many":[30,234],"nonlocal":[31,121,145],"similar":[32,146],"patches":[33,147],"can":[34],"be":[35],"searched":[36],"across":[37],"whole":[39],"for":[41,115,182],"a":[42,112,149,154,183,206],"given":[43],"patch,":[44],"which":[45,118],"significantly":[47],"boosted":[48],"performance":[50],"of":[51,57,68,92,129,185,193,201],"restoration.":[53],"To":[54],"best":[56,178],"our":[58],"knowledge,":[59],"most":[60],"existing":[61],"methods":[62,75,237],"only":[63],"consider":[64],"NSS":[66,78,90,127,168],"prior":[67,79],"input":[70,93,132],"degraded":[71,94,133],"image,":[72],"while":[73],"few":[74],"exploit":[76],"from":[80,148,176],"external":[81,97,136,167],"clean":[82,98,137,150],"corpus.":[84,139],"However,":[85],"how":[86],"to":[87,164,214],"utilize":[88],"priors":[91,128],"corpus":[100],"simultaneously":[101],"is":[102,162,174,212],"still":[103],"an":[104,166,171,209],"open":[105],"problem.":[106],"In":[107],"this":[108],"paper,":[109],"we":[110,141],"propose":[111],"novel":[113],"approach":[114],"exploits":[119],"simultaneous":[120],"(SNSS)":[123],"integrating":[125,190],"both":[130,239],"Firstly,":[140],"search":[142],"group":[144,173,184,192,200],"corpus,":[152],"group-based":[155],"Gaussian":[156,180,203],"Mixture":[157],"Model":[158],"(GMM)":[159],"learning":[160],"algorithm":[161,211],"developed":[163,213],"learn":[165],"prior.":[169],"Then,":[170],"optimal":[172],"selected":[175],"suitable":[179],"component":[181,204],"noisy":[187,195],"image.":[188],"By":[189],"corresponding":[199],"with":[205,233],"low-rank":[207],"constraint,":[208],"iterative":[210],"solve":[215],"proposed":[217,225],"SNSS":[218],"model.":[219],"Experimental":[220],"results":[221,231],"demonstrate":[222],"that":[223],"SNSS-based":[226],"denoising":[227,236],"method":[228],"produces":[229],"superior":[230],"compared":[232],"state-of-the-art":[235],"objective":[240],"perceptual":[242],"quality.":[243]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
