{"id":"https://openalex.org/W2891654669","doi":"https://doi.org/10.1109/icip.2018.8451585","title":"Efficient Algorithm for Convolutional Dictionary Learning via Accelerated Proximal Gradient Consensus","display_name":"Efficient Algorithm for Convolutional Dictionary Learning via Accelerated Proximal Gradient Consensus","publication_year":2018,"publication_date":"2018-09-07","ids":{"openalex":"https://openalex.org/W2891654669","doi":"https://doi.org/10.1109/icip.2018.8451585","mag":"2891654669"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2018.8451585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5032775480","display_name":"Gustavo Silva","orcid":"https://orcid.org/0000-0002-9497-8901"},"institutions":[{"id":"https://openalex.org/I65285256","display_name":"Pontificia Universidad Cat\u00f3lica del Per\u00fa","ror":"https://ror.org/00013q465","country_code":"PE","type":"education","lineage":["https://openalex.org/I65285256"]}],"countries":["PE"],"is_corresponding":false,"raw_author_name":"Gustavo Silva","raw_affiliation_strings":["Electrical Engineering Department, Pontificia Universidad Cat6lica del Peru, Lima, Peru"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, Pontificia Universidad Cat6lica del Peru, Lima, Peru","institution_ids":["https://openalex.org/I65285256"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077092609","display_name":"Paul Rodr\u00edguez","orcid":"https://orcid.org/0000-0002-8501-0907"},"institutions":[{"id":"https://openalex.org/I65285256","display_name":"Pontificia Universidad Cat\u00f3lica del Per\u00fa","ror":"https://ror.org/00013q465","country_code":"PE","type":"education","lineage":["https://openalex.org/I65285256"]}],"countries":["PE"],"is_corresponding":false,"raw_author_name":"Paul Rodriguez","raw_affiliation_strings":["Electrical Engineering Department, Pontificia Universidad Cat6lica del Peru, Lima, Peru"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, Pontificia Universidad Cat6lica del Peru, Lima, Peru","institution_ids":["https://openalex.org/I65285256"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I65285256"],"apc_list":null,"apc_paid":null,"fwci":2.1902,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.93107605,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3978","last_page":"3982"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9995999932289124,"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/computer-science","display_name":"Computer science","score":0.7948862314224243},{"id":"https://openalex.org/keywords/inpainting","display_name":"Inpainting","score":0.7503470182418823},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5742520689964294},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.475339412689209},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.40205344557762146},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3515355885028839},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33526158332824707}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7948862314224243},{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.7503470182418823},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5742520689964294},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.475339412689209},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40205344557762146},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3515355885028839},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33526158332824707}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2018.8451585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1528703258","https://openalex.org/W1980119385","https://openalex.org/W2008732654","https://openalex.org/W2029507661","https://openalex.org/W2115429828","https://openalex.org/W2117259536","https://openalex.org/W2138658730","https://openalex.org/W2141200610","https://openalex.org/W2153663612","https://openalex.org/W2164278908","https://openalex.org/W2170942078","https://openalex.org/W2190662802","https://openalex.org/W2293078015","https://openalex.org/W2362164969","https://openalex.org/W2720595576","https://openalex.org/W2733076523","https://openalex.org/W2756218017","https://openalex.org/W2790083234","https://openalex.org/W2800754404","https://openalex.org/W2913535645","https://openalex.org/W3105304071","https://openalex.org/W3144571769","https://openalex.org/W4206310440","https://openalex.org/W4244393449","https://openalex.org/W4250297470","https://openalex.org/W4292363360","https://openalex.org/W6751356913"],"related_works":["https://openalex.org/W2017457812","https://openalex.org/W3178025616","https://openalex.org/W2060947339","https://openalex.org/W2131831293","https://openalex.org/W2946160871","https://openalex.org/W3035059915","https://openalex.org/W1995073329","https://openalex.org/W425542480","https://openalex.org/W49967185","https://openalex.org/W2107727507"],"abstract_inverted_index":{"Convolutional":[0],"sparse":[1],"representations":[2],"are":[3],"receiving":[4],"an":[5,97],"increase":[6],"attention":[7],"as":[8,52],"a":[9,88,115],"better":[10],"alternative":[11],"to":[12,38,62,91,113],"the":[13,41,55,78,83,107,121,131,138],"standard":[14],"patch-based":[15],"formulation":[16],"for":[17],"multiple":[18],"image":[19],"processing":[20],"tasks.":[21,162],"Several":[22],"different":[23],"algorithms":[24],"based":[25,95],"on":[26,69,96],"ADMM,":[27],"ADMM":[28,48,108],"consensus":[29,49,99,109,140],"and":[30,110,145,155,160],"APG":[31,98,111],"(Accelerated":[32],"Proximal":[33],"Gradient)":[34],"have":[35],"been":[36],"proposed":[37,132],"efficiently":[39],"solve":[40],"convolutional":[42],"dictionary":[43,79],"learning":[44],"problem.":[45],"Among":[46],"them,":[47],"is":[50,74,134],"considered":[51],"one":[53],"of":[54,72,106,153],"fastest":[56],"methods":[57],"implemented":[58,142],"in":[59,126,143,151,158],"parallel":[60,146],"due":[61],"its":[63,67],"separable":[64],"structure.":[65],"However,":[66],"usage":[68],"large":[70],"sets":[71],"images":[73],"computationally":[75],"restricted":[76],"by":[77],"update":[80],"stage.":[81],"In":[82],"present":[84],"work,":[85],"we":[86],"propose":[87],"novel":[89],"method":[90,102,133,141],"address":[92],"this":[93],"stage":[94],"approach.":[100],"This":[101],"considers":[103],"particular":[104],"strategies":[105],"frameworks":[112],"develop":[114],"less":[116],"complex":[117],"solution":[118],"decoupled":[119],"across":[120],"training":[122],"images.":[123],"We":[124],"show":[125],"our":[127],"experimental":[128],"results":[129],"that":[130],"significantly":[135],"faster":[136],"than":[137],"state-of-the-art":[139],"serial":[144],"while":[147],"maintaining":[148],"comparable":[149],"performance":[150],"terms":[152],"reconstruction":[154],"sparsity":[156],"metrics":[157],"denoising":[159],"inpainting":[161]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}