{"id":"https://openalex.org/W2921841286","doi":"https://doi.org/10.23919/apsipa.2018.8659548","title":"Block-Matching Convolutional Neural Network (BMCNN): Improving CNN-Based Denoising by Block-Matched Inputs","display_name":"Block-Matching Convolutional Neural Network (BMCNN): Improving CNN-Based Denoising by Block-Matched Inputs","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2921841286","doi":"https://doi.org/10.23919/apsipa.2018.8659548","mag":"2921841286"},"language":"en","primary_location":{"id":"doi:10.23919/apsipa.2018.8659548","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659548","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/A5052386336","display_name":"Byeongyong Ahn","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Byeongyong Ahn","raw_affiliation_strings":["Samsung Electronics Co., Ltd, Suwon, Korea"],"affiliations":[{"raw_affiliation_string":"Samsung Electronics Co., Ltd, Suwon, Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083360792","display_name":"Yoonsik Kim","orcid":"https://orcid.org/0000-0001-8023-8278"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yoonsik Kim","raw_affiliation_strings":["INMC, Seoul National University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"INMC, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011503013","display_name":"Guyong Park","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Guyong Park","raw_affiliation_strings":["INMC, Seoul National University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"INMC, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055171648","display_name":"Nam Ik Cho","orcid":"https://orcid.org/0000-0001-5297-4649"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Nam Ik Cho","raw_affiliation_strings":["INMC, Seoul National University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"INMC, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5052386336"],"corresponding_institution_ids":["https://openalex.org/I2250650973"],"apc_list":null,"apc_paid":null,"fwci":0.6379,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.7574892,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"516","last_page":"525"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","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/T10688","display_name":"Image and Signal Denoising Methods","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/T11105","display_name":"Advanced Image Processing Techniques","score":0.998199999332428,"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":0.9958000183105469,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.818588137626648},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.7801406383514404},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7170739769935608},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.7121080160140991},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6940100789070129},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6736067533493042},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5243240594863892},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4743731617927551},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4642767310142517},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4284115731716156},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33387261629104614},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33347609639167786},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16525667905807495}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.818588137626648},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.7801406383514404},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7170739769935608},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.7121080160140991},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6940100789070129},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6736067533493042},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5243240594863892},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4743731617927551},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4642767310142517},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4284115731716156},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33387261629104614},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33347609639167786},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16525667905807495},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/apsipa.2018.8659548","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659548","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"},{"id":"pmh:oai:s-space.snu.ac.kr:10371/186825","is_oa":false,"landing_page_url":"https://hdl.handle.net/10371/186825","pdf_url":null,"source":{"id":"https://openalex.org/S4306401345","display_name":"Seoul National University Open Repository (Seoul National University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139264467","host_organization_name":"Seoul National University","host_organization_lineage":["https://openalex.org/I139264467"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6000000238418579,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W36249753","https://openalex.org/W54257720","https://openalex.org/W935139217","https://openalex.org/W1488355786","https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1745334888","https://openalex.org/W1836465849","https://openalex.org/W1906770428","https://openalex.org/W1973567017","https://openalex.org/W1978749115","https://openalex.org/W2014311222","https://openalex.org/W2037642501","https://openalex.org/W2048695508","https://openalex.org/W2056370875","https://openalex.org/W2097073572","https://openalex.org/W2098477387","https://openalex.org/W2107878631","https://openalex.org/W2112796928","https://openalex.org/W2118550318","https://openalex.org/W2121058967","https://openalex.org/W2135065661","https://openalex.org/W2142683286","https://openalex.org/W2153663612","https://openalex.org/W2155893237","https://openalex.org/W2194775991","https://openalex.org/W2214802144","https://openalex.org/W2242218935","https://openalex.org/W2284050935","https://openalex.org/W2508457857","https://openalex.org/W2515222661","https://openalex.org/W2520164769","https://openalex.org/W2536599074","https://openalex.org/W2562637781","https://openalex.org/W2608488581","https://openalex.org/W2963129783","https://openalex.org/W2963685250","https://openalex.org/W2964046669","https://openalex.org/W2964121744","https://openalex.org/W4242059867","https://openalex.org/W6601457164","https://openalex.org/W6602211262","https://openalex.org/W6624640001","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6674855086","https://openalex.org/W6687483927","https://openalex.org/W6695676441","https://openalex.org/W6726381175","https://openalex.org/W6728784816","https://openalex.org/W6730551332"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W1972035260","https://openalex.org/W4312417841","https://openalex.org/W2099811626"],"abstract_inverted_index":{"There":[0],"are":[1,28,73],"two":[2],"main":[3],"streams":[4],"in":[5,69],"up-to-date":[6],"image":[7,72,104],"denoising":[8,96,117],"algorithms:":[9],"non-local":[10],"self":[11],"similarity":[12],"(NSS)":[13],"prior":[14,62],"based":[15,22,26,40],"methods":[16,27,41],"and":[17,34,63,115,146],"convolutional":[18,54],"neural":[19,55],"network":[20,56],"(CNN)":[21],"methods.":[23],"The":[24,102],"NSS":[25,61],"favorable":[29],"on":[30,44,98],"images":[31],"with":[32],"regular":[33],"repetitive":[35,145],"patterns":[36],"while":[37],"the":[38,70,83,88,99,112,120,132],"CNN":[39,126],"perform":[42],"better":[43],"irregular":[45,147],"structures.":[46,148],"In":[47,79,139],"this":[48],"paper,":[49],"we":[50,91],"propose":[51],"a":[52,76,108,125],"block-matching":[53],"(BMCNN)":[57],"method":[58],"that":[59,131],"combines":[60],"CNN.":[64],"Initially,":[65],"similar":[66],"local":[67],"patches":[68],"input":[71],"integrated":[74],"into":[75],"3D":[77],"block.":[78],"order":[80],"to":[81],"prevent":[82],"noise":[84],"from":[85],"messing":[86],"up":[87],"block":[89,113,121],"matching,":[90,114],"first":[92],"apply":[93],"an":[94],"existing":[95],"algorithm":[97,135],"noisy":[100],"image.":[101],"denoised":[103],"is":[105,122],"employed":[106],"as":[107],"pilot":[109],"signal":[110],"for":[111,119],"then":[116],"function":[118],"learned":[123],"by":[124],"structure.":[127],"Experimental":[128],"results":[129],"show":[130],"proposed":[133],"BMCNN":[134,141],"achieves":[136],"state-of-the-art":[137],"performance.":[138],"detail,":[140],"can":[142],"restore":[143],"both":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
