{"id":"https://openalex.org/W2793381735","doi":"https://doi.org/10.1109/vcip.2017.8305145","title":"Multi-scale mutual feature convolutional neural network for depth image denoise and enhancement","display_name":"Multi-scale mutual feature convolutional neural network for depth image denoise and enhancement","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2793381735","doi":"https://doi.org/10.1109/vcip.2017.8305145","mag":"2793381735"},"language":"en","primary_location":{"id":"doi:10.1109/vcip.2017.8305145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2017.8305145","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Visual Communications and Image Processing (VCIP)","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/A5101514887","display_name":"Xuan Liao","orcid":"https://orcid.org/0000-0002-7261-3821"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xuan Liao","raw_affiliation_strings":["School of Electronics and Information, South China University of Technology, Guangzhou, GD, P.R. China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, South China University of Technology, Guangzhou, GD, P.R. China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100327609","display_name":"Xin Zhang","orcid":"https://orcid.org/0000-0003-3636-6453"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Zhang","raw_affiliation_strings":["School of Electronics and Information, South China University of Technology, Guangzhou, GD, P.R. China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, South China University of Technology, Guangzhou, GD, P.R. China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101514887"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.6471,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.79711356,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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":0.9997000098228455,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9995999932289124,"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/mutual-information","display_name":"Mutual information","score":0.7854620814323425},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7681804895401001},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.731464147567749},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6645224094390869},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6641532778739929},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6567582488059998},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5861228704452515},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5099984407424927},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.4211077094078064}],"concepts":[{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.7854620814323425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7681804895401001},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.731464147567749},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6645224094390869},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6641532778739929},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6567582488059998},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5861228704452515},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5099984407424927},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.4211077094078064},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip.2017.8305145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2017.8305145","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W54257720","https://openalex.org/W125693051","https://openalex.org/W1522301498","https://openalex.org/W1878947057","https://openalex.org/W1966600789","https://openalex.org/W1993120651","https://openalex.org/W1994184495","https://openalex.org/W2021191215","https://openalex.org/W2136154655","https://openalex.org/W2139034512","https://openalex.org/W2146337213","https://openalex.org/W2155479981","https://openalex.org/W2159203540","https://openalex.org/W2199472553","https://openalex.org/W2402704303","https://openalex.org/W2520808298","https://openalex.org/W6602211262","https://openalex.org/W6605121731","https://openalex.org/W6631190155","https://openalex.org/W6639138679","https://openalex.org/W6681686951","https://openalex.org/W6713383345"],"related_works":["https://openalex.org/W2466816617","https://openalex.org/W1970834875","https://openalex.org/W3174028392","https://openalex.org/W842936808","https://openalex.org/W2000517284","https://openalex.org/W2136503713","https://openalex.org/W2365318811","https://openalex.org/W4293226380","https://openalex.org/W2375330620","https://openalex.org/W2913710507"],"abstract_inverted_index":{"RGB-D":[0],"images":[1],"captured":[2],"by":[3],"consumer":[4],"camera":[5],"can":[6],"provide":[7],"pair-wise":[8],"color":[9],"and":[10,20,36,70,82,102,134],"depth":[11,14,31,71,83,98,108,118],"information":[12],"but":[13],"image":[15],"usually":[16],"contains":[17],"strong":[18],"noise":[19],"large":[21],"holes.":[22],"Due":[23],"to":[24,53,95,115],"different":[25],"modalities":[26],"of":[27,58,130,146],"RGB-D,":[28],"the":[29,45,97],"intensity-guided":[30],"enhancement":[32],"easily":[33],"causes":[34],"artifacts":[35],"blurred":[37],"edges.":[38],"To":[39],"solve":[40],"this":[41],"problem,":[42],"we":[43,74,88],"propose":[44],"multi-scale":[46,91],"mutual":[47,56,67,84,92,99,113],"feature":[48,68,85,93,100],"convolutional":[49],"neural":[50],"network":[51],"(MSMF-CNN)":[52],"learn":[54],"essential":[55],"features":[57,114],"RGB-D.":[59],"This":[60],"end-to-end":[61],"framework":[62],"has":[63],"two":[64,76,112,125],"stages,":[65],"i.e.,":[66],"learning":[69,101],"regeneration.":[72],"Firstly,":[73],"design":[75,89],"parallel":[77],"separated":[78],"subnetworks":[79],"as":[80],"intensity":[81],"generator.":[86],"Specifically,":[87],"a":[90],"generator":[94],"enforce":[96],"incorporate":[103],"various":[104],"structure":[105],"characteristic.":[106],"Secondly,":[107],"transformation":[109],"sub-network":[110],"combines":[111],"recover":[116],"clean":[117],"image.":[119],"We":[120],"tested":[121],"our":[122,141],"model":[123],"on":[124],"well-known":[126],"datasets":[127],"in":[128],"terms":[129],"PSNR,":[131],"visual":[132],"effect":[133],"computing":[135],"speed.":[136],"Compared":[137],"with":[138],"state-of-art":[139],"algorithms,":[140],"method":[142],"provides":[143],"better":[144],"performance":[145],"all":[147],"three":[148],"criteria.":[149]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
