{"id":"https://openalex.org/W2993222765","doi":"https://doi.org/10.1109/biocas.2019.8918994","title":"A CNN-Based Blind Denoising Method for Endoscopic Images","display_name":"A CNN-Based Blind Denoising Method for Endoscopic Images","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2993222765","doi":"https://doi.org/10.1109/biocas.2019.8918994","mag":"2993222765"},"language":"en","primary_location":{"id":"doi:10.1109/biocas.2019.8918994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/biocas.2019.8918994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2003.06986","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012545205","display_name":"Shaofeng Zou","orcid":"https://orcid.org/0000-0002-2821-6941"},"institutions":[{"id":"https://openalex.org/I4210119392","display_name":"Institute of Microelectronics","ror":"https://ror.org/02s6gs133","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210119392"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaofeng Zou","raw_affiliation_strings":["Institute of Microelectronics, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Microelectronics, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089","https://openalex.org/I4210119392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013670234","display_name":"Mingzhu Long","orcid":"https://orcid.org/0000-0001-6681-550X"},"institutions":[{"id":"https://openalex.org/I4210119392","display_name":"Institute of Microelectronics","ror":"https://ror.org/02s6gs133","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210119392"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingzhu Long","raw_affiliation_strings":["Institute of Microelectronics, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Microelectronics, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089","https://openalex.org/I4210119392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100451331","display_name":"Xuyang Wang","orcid":"https://orcid.org/0000-0001-5718-0231"},"institutions":[{"id":"https://openalex.org/I4210119392","display_name":"Institute of Microelectronics","ror":"https://ror.org/02s6gs133","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210119392"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuyang Wang","raw_affiliation_strings":["Institute of Microelectronics, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Microelectronics, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089","https://openalex.org/I4210119392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063333627","display_name":"Xiang Xie","orcid":"https://orcid.org/0000-0002-4277-574X"},"institutions":[{"id":"https://openalex.org/I4210119392","display_name":"Institute of Microelectronics","ror":"https://ror.org/02s6gs133","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210119392"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Xie","raw_affiliation_strings":["Institute of Microelectronics, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Microelectronics, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089","https://openalex.org/I4210119392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101764542","display_name":"Guolin Li","orcid":"https://orcid.org/0000-0001-9934-4230"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guolin Li","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100356864","display_name":"Zhihua Wang","orcid":"https://orcid.org/0000-0001-6567-0759"},"institutions":[{"id":"https://openalex.org/I4210119392","display_name":"Institute of Microelectronics","ror":"https://ror.org/02s6gs133","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210119392"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihua Wang","raw_affiliation_strings":["Institute of Microelectronics, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Microelectronics, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089","https://openalex.org/I4210119392"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5084,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.70414285,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"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/T10688","display_name":"Image and Signal Denoising Methods","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/T10688","display_name":"Image and Signal Denoising Methods","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":0.9990000128746033,"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/T11165","display_name":"Image and Video Quality Assessment","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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7721571922302246},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7712448835372925},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6699806451797485},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6592913866043091},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5468825697898865},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5440305471420288},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.5242239236831665},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4726989269256592},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.45128968358039856},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.44521364569664},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.42911043763160706},{"id":"https://openalex.org/keywords/capsule-endoscopy","display_name":"Capsule endoscopy","score":0.4224562346935272},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.410597026348114},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.40081578493118286},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3565523624420166},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3227798342704773},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.07673916220664978},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.06996262073516846}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7721571922302246},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7712448835372925},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6699806451797485},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6592913866043091},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5468825697898865},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5440305471420288},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.5242239236831665},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4726989269256592},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.45128968358039856},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.44521364569664},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.42911043763160706},{"id":"https://openalex.org/C2777333622","wikidata":"https://www.wikidata.org/wiki/Q116753","display_name":"Capsule endoscopy","level":2,"score":0.4224562346935272},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.410597026348114},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40081578493118286},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3565523624420166},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3227798342704773},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.07673916220664978},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.06996262073516846},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/biocas.2019.8918994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/biocas.2019.8918994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2003.06986","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.06986","pdf_url":"https://arxiv.org/pdf/2003.06986","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2003.06986","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.06986","pdf_url":"https://arxiv.org/pdf/2003.06986","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1993205988","https://openalex.org/W2027898483","https://openalex.org/W2047710600","https://openalex.org/W2469031810","https://openalex.org/W2612445135","https://openalex.org/W2754213847","https://openalex.org/W2794500848","https://openalex.org/W2795722336","https://openalex.org/W3103635814"],"related_works":["https://openalex.org/W2609695940","https://openalex.org/W2608489025","https://openalex.org/W182326524","https://openalex.org/W3108403339","https://openalex.org/W2162712524","https://openalex.org/W4287867034","https://openalex.org/W2175872006","https://openalex.org/W3005783148","https://openalex.org/W3021143552","https://openalex.org/W2004821155"],"abstract_inverted_index":{"The":[0,50,127,140],"quality":[1,113,122,129,180],"of":[2,17,123,142,168,178],"images":[3,27,160,164],"captured":[4],"by":[5,148],"wireless":[6],"capsule":[7],"endoscopy":[8],"(WCE)":[9],"is":[10,66,130,144],"key":[11],"for":[12,79],"doctors":[13],"to":[14,29,68,89,119,132],"diagnose":[15],"diseases":[16],"gastrointestinal":[18],"(GI)":[19],"tract.":[20,38],"However,":[21],"there":[22],"exist":[23],"many":[24],"low-quality":[25],"endoscopic":[26,80,159],"due":[28],"the":[30,43,63,121,124,134,166,173],"limited":[31],"illumination":[32],"and":[33,59,104,161,181],"complex":[34],"environment":[35],"in":[36,137,152,176],"GI":[37,56],"After":[39],"an":[40,47],"enhancement":[41],"process,":[42],"severe":[44],"noise":[45,51,64,102],"become":[46],"unacceptable":[48],"problem.":[49],"varies":[52],"with":[53],"different":[54],"cameras,":[55],"tract":[57],"environments":[58],"image":[60,93,98,112],"enhancement.":[61],"And":[62],"model":[65,103],"hard":[67],"be":[69],"obtained.":[70],"This":[71],"paper":[72],"proposes":[73],"a":[74,91,96,100,110],"convolutional":[75],"blind":[76,111],"denoising":[77],"network":[78,115],"images.":[81,126],"We":[82],"apply":[83],"Deep":[84],"Image":[85],"Prior":[86],"(DIP)":[87],"method":[88,171],"reconstruct":[90],"clean":[92],"iteratively":[94],"using":[95,149],"noisy":[97,163],"without":[99],"specific":[101],"ground":[105],"truth.":[106],"Then":[107],"we":[108],"design":[109],"assessment":[114],"based":[116],"on":[117,158],"MobileNet":[118],"estimate":[120],"reconstructed":[125],"estimated":[128],"used":[131],"stop":[133],"iterative":[135],"operation":[136],"DIP":[138,154],"method.":[139],"number":[141],"iterations":[143],"reduced":[145],"about":[146],"36%":[147],"transfer":[150],"learning":[151],"our":[153,169],"process.":[155],"Experimental":[156],"results":[157],"real-world":[162],"demonstrate":[165],"superiority":[167],"proposed":[170],"over":[172],"state-of-the-art":[174],"methods":[175],"terms":[177],"visual":[179],"quantitative":[182],"metrics.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}