{"id":"https://openalex.org/W2889933451","doi":"https://doi.org/10.1109/vcip.2018.8698674","title":"Multi-Scale Deep Compressive Sensing Network","display_name":"Multi-Scale Deep Compressive Sensing Network","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2889933451","doi":"https://doi.org/10.1109/vcip.2018.8698674","mag":"2889933451"},"language":"en","primary_location":{"id":"doi:10.1109/vcip.2018.8698674","doi":"https://doi.org/10.1109/vcip.2018.8698674","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2018.8698674","pdf_url":null,"source":{"id":"https://openalex.org/S4363608486","display_name":"IEEE International Conference on Visual Communications and Image Processing (VCIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2018 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1809.05717","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034124470","display_name":"Thuong Nguyen Canh","orcid":"https://orcid.org/0000-0002-7880-6659","observed_orcids":["https://orcid.org/0000-0002-7880-6659"]},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Thuong Nguyen Canh","raw_affiliation_strings":["Department of Electronics and Computer Engineering, Sungkyunkwan University, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Computer Engineering, Sungkyunkwan University, Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074587654","display_name":"Byeungwoo Jeon","orcid":"https://orcid.org/0000-0002-5650-2881","observed_orcids":["https://orcid.org/0000-0002-5650-2881"]},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byeungwoo Jeon","raw_affiliation_strings":["Department of Electronics and Computer Engineering, Sungkyunkwan University, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Computer Engineering, Sungkyunkwan University, Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I848706"],"apc_list":null,"apc_paid":null,"fwci":2.2067,"has_fulltext":true,"cited_by_count":28,"citation_normalized_percentile":{"value":0.8959276,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9980000257492065,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9980000257492065,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.0003000000142492354,"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/T11210","display_name":"Mathematical Analysis and Transform Methods","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multi-scale-sampling","display_name":"multi-scale sampling","score":0.890999972820282},{"id":"https://openalex.org/keywords/multi-scale-reconstruction","display_name":"multi-scale reconstruction","score":0.7239999771118164},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"wavelet transform","score":0.8330000042915344},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8859999775886536},{"id":"https://openalex.org/keywords/image-reconstruction","display_name":"image reconstruction","score":0.8289999961853027}],"concepts":[{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7403148412704468},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6968986988067627},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6853940486907959},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6625958681106567},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6480436325073242},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6346125602722168},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6309701204299927},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6309671998023987},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5873866081237793},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5566900372505188},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.528792679309845},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5166873931884766},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4287313222885132},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.4175061881542206},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41480740904808044},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2973176836967468},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.21174931526184082},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1759432554244995},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.11343526840209961},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/vcip.2018.8698674","doi":"https://doi.org/10.1109/vcip.2018.8698674","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2018.8698674","pdf_url":null,"source":{"id":"https://openalex.org/S4363608486","display_name":"IEEE International Conference on Visual Communications and Image Processing (VCIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2018 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1809.05717","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1809.05717","pdf_url":"https://arxiv.org/pdf/1809.05717","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},{"id":"doi:10.48550/arxiv.1809.05717","doi":"https://doi.org/10.48550/arxiv.1809.05717","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1809.05717","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1809.05717","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1809.05717","pdf_url":"https://arxiv.org/pdf/1809.05717","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2889933451.pdf","grobid_xml":"https://content.openalex.org/works/W2889933451.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1502593327","https://openalex.org/W1963882359","https://openalex.org/W1977252496","https://openalex.org/W2001380973","https://openalex.org/W2045518970","https://openalex.org/W2045737896","https://openalex.org/W2129812098","https://openalex.org/W2273561594","https://openalex.org/W2294661604","https://openalex.org/W2296616510","https://openalex.org/W2474424652","https://openalex.org/W2508457857","https://openalex.org/W2532761202","https://openalex.org/W2590877996","https://openalex.org/W2592978821","https://openalex.org/W2739757502","https://openalex.org/W2806506812","https://openalex.org/W2963494934","https://openalex.org/W4250955649","https://openalex.org/W6733814211"],"related_works":["https://openalex.org/W4212801282","https://openalex.org/W7160597229","https://openalex.org/W4292387271","https://openalex.org/W3103321200","https://openalex.org/W2891447304","https://openalex.org/W2970910123","https://openalex.org/W2980149757","https://openalex.org/W4298052068","https://openalex.org/W7204760626","https://openalex.org/W4393148512"],"abstract_inverted_index":{"With":[0],"joint":[1],"learning":[2],"of":[3],"sampling":[4,40,110],"and":[5,19,111],"recovery,":[6],"the":[7,38,98],"deep":[8],"learning-based":[9],"compressive":[10],"sensing":[11],"(DCS)":[12],"has":[13],"shown":[14],"significant":[15],"improvement":[16],"in":[17,37,60,116],"performance":[18],"running":[20],"time":[21],"reduction.":[22],"Its":[23],"reconstructed":[24,83],"image,":[25],"however,":[26],"losses":[27],"high-frequency":[28],"content":[29],"especially":[30],"at":[31],"low":[32],"subrates.":[33],"This":[34],"happens":[35],"similarly":[36],"multi-scale":[39,54,89,109,112],"scheme":[41],"which":[42,61],"also":[43],"samples":[44],"more":[45],"low-frequency":[46],"components.":[47],"In":[48],"this":[49],"paper,":[50],"we":[51,62],"propose":[52],"a":[53],"DCS":[55],"convolutional":[56],"neural":[57],"network":[58,103],"(MS-DCSNet)":[59],"convert":[63],"image":[64,84],"signal":[65],"using":[66],"multiple":[67],"scale-based":[68],"wavelet":[69,92],"transform,":[70],"then":[71],"capture":[72],"it":[73],"through":[74],"convolution":[75,93],"block":[76,78],"by":[77],"across":[79],"scales.":[80],"The":[81,102],"initial":[82],"is":[85,94,104],"directly":[86],"recovered":[87],"from":[88],"measurements.":[90],"Multi-scale":[91],"utilized":[95],"to":[96,106],"enhance":[97],"final":[99],"reconstruction":[100,118],"quality.":[101,119],"able":[105],"learn":[107],"both":[108],"reconstruction,":[113],"thus":[114],"results":[115],"better":[117]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2018-09-27T00:00:00"}
