{"id":"https://openalex.org/W2897567301","doi":"https://doi.org/10.1109/ijcnn.2018.8489672","title":"Combined Convolutional Neural Network for High Frequency Restoration in Acoustic Impedance Images","display_name":"Combined Convolutional Neural Network for High Frequency Restoration in Acoustic Impedance Images","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2897567301","doi":"https://doi.org/10.1109/ijcnn.2018.8489672","mag":"2897567301"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489672","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","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/A5052511284","display_name":"Isaac Sacramento","orcid":"https://orcid.org/0000-0002-8341-0501"},"institutions":[{"id":"https://openalex.org/I4104125","display_name":"Universidade Federal de Santa Catarina","ror":"https://ror.org/041akq887","country_code":"BR","type":"education","lineage":["https://openalex.org/I4104125"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Isaac Sacramento","raw_affiliation_strings":["Department of Informatic and Statistics, Federal University of Santa Catarina, Florianpolis, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatic and Statistics, Federal University of Santa Catarina, Florianpolis, Brazil","institution_ids":["https://openalex.org/I4104125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090517396","display_name":"Mauro Roisenberg","orcid":"https://orcid.org/0000-0001-9707-0360"},"institutions":[{"id":"https://openalex.org/I4104125","display_name":"Universidade Federal de Santa Catarina","ror":"https://ror.org/041akq887","country_code":"BR","type":"education","lineage":["https://openalex.org/I4104125"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Mauro Roisenberg","raw_affiliation_strings":["Department of Informatic and Statistics, Federal University of Santa Catarina, Florianpolis, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatic and Statistics, Federal University of Santa Catarina, Florianpolis, Brazil","institution_ids":["https://openalex.org/I4104125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079691666","display_name":"Rodrigo Exterkoetter","orcid":"https://orcid.org/0000-0002-3639-1635"},"institutions":[{"id":"https://openalex.org/I4104125","display_name":"Universidade Federal de Santa Catarina","ror":"https://ror.org/041akq887","country_code":"BR","type":"education","lineage":["https://openalex.org/I4104125"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rodrigo Exterkoetter","raw_affiliation_strings":["Department of Informatic and Statistics, Federal University of Santa Catarina, Florianpolis, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatic and Statistics, Federal University of Santa Catarina, Florianpolis, Brazil","institution_ids":["https://openalex.org/I4104125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073351837","display_name":"Leando P. de Figueiredo","orcid":null},"institutions":[{"id":"https://openalex.org/I4104125","display_name":"Universidade Federal de Santa Catarina","ror":"https://ror.org/041akq887","country_code":"BR","type":"education","lineage":["https://openalex.org/I4104125"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Leando P. de Figueiredo","raw_affiliation_strings":["Department of Physics, Federal University of Santa Catarina, Florianpolis, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Physics, Federal University of Santa Catarina, Florianpolis, Brazil","institution_ids":["https://openalex.org/I4104125"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061923106","display_name":"Bruno Barbosa Rodrigues","orcid":"https://orcid.org/0000-0002-6127-8212"},"institutions":[{"id":"https://openalex.org/I32393484","display_name":"Petrobras (Brazil)","ror":"https://ror.org/0235kyq22","country_code":"BR","type":"company","lineage":["https://openalex.org/I32393484"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Bruno Rodrigues","raw_affiliation_strings":["Petrobras Research Center, Petrobras, Rio de Janeiro, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Petrobras Research Center, Petrobras, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I32393484"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.423,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63610378,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T10892","display_name":"Drilling and Well Engineering","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/computer-science","display_name":"Computer science","score":0.7728812098503113},{"id":"https://openalex.org/keywords/deblurring","display_name":"Deblurring","score":0.7535499334335327},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7474122047424316},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6223427653312683},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5221477150917053},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5079920887947083},{"id":"https://openalex.org/keywords/electrical-impedance","display_name":"Electrical impedance","score":0.44826823472976685},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4361454248428345},{"id":"https://openalex.org/keywords/simple-linear-regression","display_name":"Simple linear regression","score":0.4292405843734741},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3461008667945862},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.3360169231891632},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.3001745343208313},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2830469608306885},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18685615062713623},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.1366346776485443},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10061177611351013}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7728812098503113},{"id":"https://openalex.org/C2777693668","wikidata":"https://www.wikidata.org/wiki/Q25053743","display_name":"Deblurring","level":5,"score":0.7535499334335327},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7474122047424316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6223427653312683},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5221477150917053},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5079920887947083},{"id":"https://openalex.org/C17829176","wikidata":"https://www.wikidata.org/wiki/Q179043","display_name":"Electrical impedance","level":2,"score":0.44826823472976685},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4361454248428345},{"id":"https://openalex.org/C149769383","wikidata":"https://www.wikidata.org/wiki/Q7520804","display_name":"Simple linear regression","level":3,"score":0.4292405843734741},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3461008667945862},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.3360169231891632},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.3001745343208313},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2830469608306885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18685615062713623},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.1366346776485443},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10061177611351013},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2018.8489672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489672","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W158587707","https://openalex.org/W1505668941","https://openalex.org/W1536680647","https://openalex.org/W1665214252","https://openalex.org/W1795014501","https://openalex.org/W1857884451","https://openalex.org/W1916935112","https://openalex.org/W1970456555","https://openalex.org/W1980287119","https://openalex.org/W1983364832","https://openalex.org/W1984859941","https://openalex.org/W1987075379","https://openalex.org/W1995562189","https://openalex.org/W2013715217","https://openalex.org/W2027355076","https://openalex.org/W2036682493","https://openalex.org/W2050847261","https://openalex.org/W2075822215","https://openalex.org/W2082667383","https://openalex.org/W2109998889","https://openalex.org/W2114388610","https://openalex.org/W2116498084","https://openalex.org/W2117539524","https://openalex.org/W2138204001","https://openalex.org/W2142263374","https://openalex.org/W2154194773","https://openalex.org/W2160786387","https://openalex.org/W2185240055","https://openalex.org/W2187997753","https://openalex.org/W2194775991","https://openalex.org/W2288122362","https://openalex.org/W2288871418","https://openalex.org/W2300657047","https://openalex.org/W2319561215","https://openalex.org/W2465552163","https://openalex.org/W2469665031","https://openalex.org/W2586628080","https://openalex.org/W2606909384","https://openalex.org/W2610481139","https://openalex.org/W2621387661","https://openalex.org/W2793045126","https://openalex.org/W2963186101","https://openalex.org/W4256129314","https://openalex.org/W4391811626","https://openalex.org/W6606497441","https://openalex.org/W6637242042","https://openalex.org/W6638563174","https://openalex.org/W6642709355","https://openalex.org/W6671359511","https://openalex.org/W6677521738","https://openalex.org/W6686904142","https://openalex.org/W6696141924","https://openalex.org/W6696148225","https://openalex.org/W6698043622","https://openalex.org/W6700008868","https://openalex.org/W6719683527","https://openalex.org/W6737061457","https://openalex.org/W6749126569"],"related_works":["https://openalex.org/W2031788393","https://openalex.org/W791927757","https://openalex.org/W2182590612","https://openalex.org/W3153582293","https://openalex.org/W2269775642","https://openalex.org/W3080537281","https://openalex.org/W2289746762","https://openalex.org/W2139384960","https://openalex.org/W2905397092","https://openalex.org/W2359633702"],"abstract_inverted_index":{"Domain-specific":[0],"methods":[1],"for":[2,134],"deblurring":[3],"particular":[4],"sorts":[5],"of":[6,16,36,71,103,131,138,162],"objects":[7],"have":[8],"gained":[9],"increasing":[10],"attention":[11],"due":[12],"to":[13,58],"the":[14,64,101,116,120,129,135,149,160,163],"ineffectiveness":[15],"generic":[17],"methods.":[18],"We":[19,75,110,126],"present":[20,112],"a":[21,68,77,95],"simple":[22],"and":[23,47,55,106,123,147],"effective":[24],"convolutional":[25,45,51],"neural":[26],"network":[27,150],"that":[28,99],"deblurs":[29],"synthetic":[30,156],"acoustic":[31,72],"impedance":[32,73],"images.":[33,109,125],"The":[34,50],"architecture":[35,151],"our":[37,91],"model":[38],"is":[39],"divided":[40],"into":[41],"two":[42],"basic":[43],"structures:":[44],"layers":[46,52,62],"regression":[48,65],"unit.":[49],"highlight":[53],"edges":[54],"contours":[56],"related":[57],"interfaces":[59],"between":[60],"rock":[61],"whereas":[63],"layer":[66],"performs":[67],"non-linear":[69],"estimation":[70],"values.":[74],"consider":[76],"training":[78,139],"dataset":[79,102],"composed":[80],"by":[81,141],"wedge":[82],"shaped":[83],"images,":[84],"positioned":[85],"at":[86],"four":[87],"different":[88],"angles.":[89],"In":[90],"work,":[92],"we":[93],"adopt":[94],"strong":[96],"supervised":[97],"learning":[98,133],"exploit":[100],"artificially":[104],"blurred":[105],"high":[107],"resolution":[108],"also":[111],"an":[113],"analysis":[114],"comparing":[115],"frequency":[117],"band-width":[118],"among":[119],"latent,":[121],"blurry,":[122],"deblurred":[124],"additionally":[127],"address":[128],"requirement":[130],"deep":[132],"huge":[136],"amount":[137],"examples":[140],"inserting":[142],"rectified":[143],"linear":[144],"units":[145],"(ReLU)":[146],"keeping":[148],"simple.":[152],"Experimental":[153],"results":[154],"on":[155],"test":[157],"images":[158],"demonstrate":[159],"efficacy":[161],"proposed":[164],"method.":[165]},"counts_by_year":[{"year":2020,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}