{"id":"https://openalex.org/W4206732155","doi":"https://doi.org/10.23919/eusipco54536.2021.9616261","title":"Removing Impulsive Noise from Color Images via a Residual Deep Neural Network Enhanced by Post-Processing","display_name":"Removing Impulsive Noise from Color Images via a Residual Deep Neural Network Enhanced by Post-Processing","publication_year":2021,"publication_date":"2021-08-23","ids":{"openalex":"https://openalex.org/W4206732155","doi":"https://doi.org/10.23919/eusipco54536.2021.9616261"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco54536.2021.9616261","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco54536.2021.9616261","pdf_url":null,"source":{"id":"https://openalex.org/S4363607854","display_name":"2021 29th European Signal Processing Conference (EUSIPCO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th European Signal Processing Conference (EUSIPCO)","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/A5033724667","display_name":"Sahar Sadrizadeh","orcid":null},"institutions":[{"id":"https://openalex.org/I133529467","display_name":"Sharif University of Technology","ror":"https://ror.org/024c2fq17","country_code":"IR","type":"education","lineage":["https://openalex.org/I133529467"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Sahar Sadrizadeh","raw_affiliation_strings":["Sharif University of Technology,Electrical Engineering Dept.,Tehran,Iran","Electrical Engineering Dept., Sharif University of Technology, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Sharif University of Technology,Electrical Engineering Dept.,Tehran,Iran","institution_ids":["https://openalex.org/I133529467"]},{"raw_affiliation_string":"Electrical Engineering Dept., Sharif University of Technology, Tehran, Iran","institution_ids":["https://openalex.org/I133529467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042428131","display_name":"Hatef Otroshi Shahreza","orcid":null},"institutions":[{"id":"https://openalex.org/I133529467","display_name":"Sharif University of Technology","ror":"https://ror.org/024c2fq17","country_code":"IR","type":"education","lineage":["https://openalex.org/I133529467"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Hatef Otroshi-Shahreza","raw_affiliation_strings":["Sharif University of Technology,Electrical Engineering Dept.,Tehran,Iran","Electrical Engineering Dept., Sharif University of Technology, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Sharif University of Technology,Electrical Engineering Dept.,Tehran,Iran","institution_ids":["https://openalex.org/I133529467"]},{"raw_affiliation_string":"Electrical Engineering Dept., Sharif University of Technology, Tehran, Iran","institution_ids":["https://openalex.org/I133529467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069159448","display_name":"Farokh Marvasti","orcid":null},"institutions":[{"id":"https://openalex.org/I133529467","display_name":"Sharif University of Technology","ror":"https://ror.org/024c2fq17","country_code":"IR","type":"education","lineage":["https://openalex.org/I133529467"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Farokh Marvasti","raw_affiliation_strings":["Sharif University of Technology,Electrical Engineering Dept.,Tehran,Iran","Electrical Engineering Dept., Sharif University of Technology, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Sharif University of Technology,Electrical Engineering Dept.,Tehran,Iran","institution_ids":["https://openalex.org/I133529467"]},{"raw_affiliation_string":"Electrical Engineering Dept., Sharif University of Technology, Tehran, Iran","institution_ids":["https://openalex.org/I133529467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5033724667"],"corresponding_institution_ids":["https://openalex.org/I133529467"],"apc_list":null,"apc_paid":null,"fwci":0.196,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.59576613,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"656","last_page":"660"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9987000226974487,"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/residual","display_name":"Residual","score":0.8268837928771973},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7917820811271667},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7067047953605652},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6981176137924194},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6813121438026428},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.559110701084137},{"id":"https://openalex.org/keywords/impulse-noise","display_name":"Impulse noise","score":0.5290129780769348},{"id":"https://openalex.org/keywords/grayscale","display_name":"Grayscale","score":0.5035435557365417},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44737282395362854},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43349286913871765},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4190952777862549},{"id":"https://openalex.org/keywords/colors-of-noise","display_name":"Colors of noise","score":0.4181000292301178},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.29342466592788696},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21937492489814758},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.1711176037788391}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8268837928771973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7917820811271667},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7067047953605652},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6981176137924194},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6813121438026428},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.559110701084137},{"id":"https://openalex.org/C127372701","wikidata":"https://www.wikidata.org/wiki/Q16979398","display_name":"Impulse noise","level":3,"score":0.5290129780769348},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.5035435557365417},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44737282395362854},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43349286913871765},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4190952777862549},{"id":"https://openalex.org/C114996537","wikidata":"https://www.wikidata.org/wiki/Q4854529","display_name":"Colors of noise","level":3,"score":0.4181000292301178},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29342466592788696},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21937492489814758},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.1711176037788391}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco54536.2021.9616261","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco54536.2021.9616261","pdf_url":null,"source":{"id":"https://openalex.org/S4363607854","display_name":"2021 29th European Signal Processing Conference (EUSIPCO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W607933207","https://openalex.org/W1781289476","https://openalex.org/W1836465849","https://openalex.org/W1996250961","https://openalex.org/W2007627285","https://openalex.org/W2060768811","https://openalex.org/W2081100746","https://openalex.org/W2086616357","https://openalex.org/W2109239774","https://openalex.org/W2122698737","https://openalex.org/W2150427434","https://openalex.org/W2162110174","https://openalex.org/W2169467627","https://openalex.org/W2282264370","https://openalex.org/W2298855392","https://openalex.org/W2337501905","https://openalex.org/W2529570353","https://openalex.org/W2531044305","https://openalex.org/W2593128366","https://openalex.org/W2607202125","https://openalex.org/W2613155248","https://openalex.org/W2767579567","https://openalex.org/W2896461726","https://openalex.org/W2899341519","https://openalex.org/W2929460997","https://openalex.org/W2935891649","https://openalex.org/W2953085622","https://openalex.org/W3003455566","https://openalex.org/W3130582922","https://openalex.org/W6638667902"],"related_works":["https://openalex.org/W115686965","https://openalex.org/W2768918307","https://openalex.org/W2110031805","https://openalex.org/W2040020606","https://openalex.org/W4362659915","https://openalex.org/W2113071088","https://openalex.org/W2321543601","https://openalex.org/W1990016983","https://openalex.org/W2972861887","https://openalex.org/W2393930098"],"abstract_inverted_index":{"Impulsive":[0],"noise":[1,7,29,54,67,95],"is":[2,153],"a":[3,19,44,72,113,118,160],"common":[4],"type":[5],"of":[6,81,84,92,103,133],"that":[8,139],"affects":[9],"gray-scale/color":[10],"images":[11,32,50],"and":[12],"videos.":[13],"In":[14],"this":[15,106],"paper,":[16],"we":[17,70,122],"present":[18],"residual":[20,40],"fully":[21],"Convolutional":[22],"Neural":[23],"Network":[24],"(CNN)":[25],"to":[26,76,128],"remove":[27],"impulsive":[28,53,94],"from":[30],"color":[31],"in":[33,96,147],"an":[34,124],"end-to-end":[35],"fashion.":[36],"We":[37],"train":[38,77],"our":[39,78,104,134,151],"CNN":[41],"model":[42],"on":[43,159],"customized":[45],"dataset":[46,58],"which":[47],"contains":[48],"noisy":[49],"with":[51],"different":[52,66],"density.":[55],"The":[56],"proposed":[57,86,141],"omits":[59],"the":[60,82,85,90,93,97,101,131,140,148],"need":[61],"for":[62,65,116],"multiple":[63],"models":[64],"densities.":[68],"Moreover,":[69],"employ":[71,123],"multi-term":[73],"loss":[74,87,114],"function":[75,88,115],"model.":[79],"One":[80],"terms":[83],"imposes":[89],"sparsity":[91],"observation":[98],"domain.":[99],"To":[100],"best":[102],"knowledge,":[105],"term":[107],"has":[108],"not":[109],"been":[110],"employed":[111],"as":[112],"training":[117],"denoising":[119],"CNN.":[120],"Finally,":[121],"iterative":[125],"post-processing":[126],"stage":[127],"further":[129],"improve":[130],"performance":[132],"method.":[135],"Simulation":[136],"results":[137],"demonstrate":[138],"approach":[142],"outperforms":[143],"other":[144],"notable":[145],"algorithms":[146],"literature.":[149],"Furthermore,":[150],"method":[152],"quite":[154],"fast,":[155],"especially":[156],"when":[157],"implemented":[158],"GPU-equipped":[161],"system.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3}],"updated_date":"2026-01-28T23:14:49.684275","created_date":"2025-10-10T00:00:00"}
