{"id":"https://openalex.org/W2625143103","doi":"https://doi.org/10.1109/icip.2017.8296555","title":"Online convolutional dictionary learning for multimodal imaging","display_name":"Online convolutional dictionary learning for multimodal imaging","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2625143103","doi":"https://doi.org/10.1109/icip.2017.8296555","mag":"2625143103"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296555","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296555","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","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/A5060106525","display_name":"K\u00e9vin Degraux","orcid":"https://orcid.org/0000-0002-2135-1454"},"institutions":[{"id":"https://openalex.org/I95674353","display_name":"UCLouvain","ror":"https://ror.org/02495e989","country_code":"BE","type":"education","lineage":["https://openalex.org/I95674353"]}],"countries":["BE"],"is_corresponding":true,"raw_author_name":"Kevin Degraux","raw_affiliation_strings":["ISPGroup/ICTEAM, Universit\u00e9 catholique de Louvain, Louvain-la-Ncuve, Belgium"],"affiliations":[{"raw_affiliation_string":"ISPGroup/ICTEAM, Universit\u00e9 catholique de Louvain, Louvain-la-Ncuve, Belgium","institution_ids":["https://openalex.org/I95674353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024602237","display_name":"Ulugbek S. Kamilov","orcid":"https://orcid.org/0000-0001-6770-3278"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ulugbek S. Kamilov","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034522188","display_name":"Petros T. Boufounos","orcid":"https://orcid.org/0000-0003-1369-0947"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Petros T. Boufounos","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052544518","display_name":"Dehong Liu","orcid":"https://orcid.org/0000-0003-3355-3018"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dehong Liu","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5060106525"],"corresponding_institution_ids":["https://openalex.org/I95674353"],"apc_list":null,"apc_paid":null,"fwci":3.8416,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.93648414,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1617","last_page":"1621"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11569","display_name":"Optical Coherence Tomography Applications","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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":0.8346419334411621},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7248225212097168},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6894495487213135},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5934613347053528},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"Dictionary learning","score":0.5545684695243835},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4692010283470154},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.46695077419281006},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4589134454727173},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.45296740531921387},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44673866033554077},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4065379798412323},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.35820332169532776}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8346419334411621},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7248225212097168},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6894495487213135},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5934613347053528},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.5545684695243835},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4692010283470154},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.46695077419281006},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4589134454727173},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.45296740531921387},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44673866033554077},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4065379798412323},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.35820332169532776},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296555","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296555","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W63091017","https://openalex.org/W1972715665","https://openalex.org/W1981989805","https://openalex.org/W2008732654","https://openalex.org/W2030978325","https://openalex.org/W2032402547","https://openalex.org/W2088254198","https://openalex.org/W2097259623","https://openalex.org/W2100705753","https://openalex.org/W2103559027","https://openalex.org/W2104600947","https://openalex.org/W2105464873","https://openalex.org/W2112447569","https://openalex.org/W2125188192","https://openalex.org/W2142224912","https://openalex.org/W2145889472","https://openalex.org/W2155966450","https://openalex.org/W2156380531","https://openalex.org/W2160547390","https://openalex.org/W2190662802","https://openalex.org/W2256685641","https://openalex.org/W2293078015","https://openalex.org/W2298375375","https://openalex.org/W2346689370","https://openalex.org/W2399453552","https://openalex.org/W2532801510","https://openalex.org/W2536599074","https://openalex.org/W2554591675","https://openalex.org/W2963657851","https://openalex.org/W4206310440","https://openalex.org/W6676727762","https://openalex.org/W6697139924","https://openalex.org/W6704634208","https://openalex.org/W6730662559"],"related_works":["https://openalex.org/W2509955295","https://openalex.org/W1987225540","https://openalex.org/W2363993830","https://openalex.org/W1778286912","https://openalex.org/W2561456314","https://openalex.org/W2249096836","https://openalex.org/W1992008660","https://openalex.org/W2116933539","https://openalex.org/W1587263836","https://openalex.org/W4245251483"],"abstract_inverted_index":{"Computational":[0],"imaging":[1],"methods":[2],"that":[3,27,64,75],"can":[4],"exploit":[5],"multiple":[6],"modalities":[7],"have":[8],"the":[9,13,66,83,89],"potential":[10],"to":[11],"enhance":[12],"capabilities":[14],"of":[15,48,69,85,91],"traditional":[16],"sensing":[17],"systems.":[18],"In":[19],"this":[20],"paper,":[21],"we":[22],"propose":[23],"a":[24,44],"new":[25],"method":[26,42],"reconstructs":[28],"multimodal":[29,57],"images":[30,49],"from":[31],"their":[32],"linear":[33],"measurements":[34],"by":[35],"exploiting":[36],"redundancies":[37],"across":[38],"different":[39],"modalities.":[40],"Our":[41],"combines":[43],"convolutional":[45,70],"group-sparse":[46],"representation":[47],"with":[50],"total":[51],"variation":[52],"(TV)":[53],"regularization":[54],"for":[55],"high-quality":[56],"imaging.":[58,94],"We":[59,81],"develop":[60],"an":[61],"online":[62],"algorithm":[63],"enables":[65],"unsupervised":[67],"learning":[68],"dictionaries":[71],"on":[72],"large-scale":[73],"datasets":[74],"are":[76],"typical":[77],"in":[78,88],"such":[79],"applications.":[80],"illustrate":[82],"benefit":[84],"our":[86],"approach":[87],"context":[90],"joint":[92],"intensity-depth":[93]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
