{"id":"https://openalex.org/W2752185112","doi":"https://doi.org/10.1109/globalsip.2017.8309136","title":"Deep learning sparse ternary projections for compressed sensing of images","display_name":"Deep learning sparse ternary projections for compressed sensing of images","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2752185112","doi":"https://doi.org/10.1109/globalsip.2017.8309136","mag":"2752185112"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2017.8309136","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2017.8309136","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1708.08311","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101961269","display_name":"Duc Minh Nguyen","orcid":"https://orcid.org/0000-0002-0367-564X"},"institutions":[{"id":"https://openalex.org/I13469542","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04","country_code":"BE","type":"education","lineage":["https://openalex.org/I13469542"]}],"countries":["BE"],"is_corresponding":true,"raw_author_name":"Duc Minh Nguyen","raw_affiliation_strings":["Vrije Universiteit Brussel, Leuven, Belgium","Vrije Universiteit Br\u00fcssel, Pleinlaan 2, B-1050 Brussels, Belgium, imec, Kapeldreef 75, B-3001 Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"Vrije Universiteit Brussel, Leuven, Belgium","institution_ids":["https://openalex.org/I13469542"]},{"raw_affiliation_string":"Vrije Universiteit Br\u00fcssel, Pleinlaan 2, B-1050 Brussels, Belgium, imec, Kapeldreef 75, B-3001 Leuven, Belgium","institution_ids":["https://openalex.org/I13469542"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076766934","display_name":"Evaggelia Tsiligianni","orcid":"https://orcid.org/0000-0003-2876-5788"},"institutions":[{"id":"https://openalex.org/I13469542","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04","country_code":"BE","type":"education","lineage":["https://openalex.org/I13469542"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Evaggelia Tsiligianni","raw_affiliation_strings":["Vrije Universiteit Brussel, Leuven, Belgium","Vrije Universiteit Br\u00fcssel, Pleinlaan 2, B-1050 Brussels, Belgium, imec, Kapeldreef 75, B-3001 Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"Vrije Universiteit Brussel, Leuven, Belgium","institution_ids":["https://openalex.org/I13469542"]},{"raw_affiliation_string":"Vrije Universiteit Br\u00fcssel, Pleinlaan 2, B-1050 Brussels, Belgium, imec, Kapeldreef 75, B-3001 Leuven, Belgium","institution_ids":["https://openalex.org/I13469542"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043511500","display_name":"Nikos Deligiannis","orcid":"https://orcid.org/0000-0001-9300-5860"},"institutions":[{"id":"https://openalex.org/I13469542","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04","country_code":"BE","type":"education","lineage":["https://openalex.org/I13469542"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Nikos Deligiannis","raw_affiliation_strings":["Vrije Universiteit Brussel, Leuven, Belgium","Vrije Universiteit Br\u00fcssel, Pleinlaan 2, B-1050 Brussels, Belgium, imec, Kapeldreef 75, B-3001 Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"Vrije Universiteit Brussel, Leuven, Belgium","institution_ids":["https://openalex.org/I13469542"]},{"raw_affiliation_string":"Vrije Universiteit Br\u00fcssel, Pleinlaan 2, B-1050 Brussels, Belgium, imec, Kapeldreef 75, B-3001 Leuven, Belgium","institution_ids":["https://openalex.org/I13469542"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101961269"],"corresponding_institution_ids":["https://openalex.org/I13469542"],"apc_list":null,"apc_paid":null,"fwci":1.2329,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.77731362,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1125","last_page":"1129"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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":1.0,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.996999979019165,"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":0.9940999746322632,"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/compressed-sensing","display_name":"Compressed sensing","score":0.9038381576538086},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6934264898300171},{"id":"https://openalex.org/keywords/ternary-operation","display_name":"Ternary operation","score":0.6426716446876526},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.6186935901641846},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5926569104194641},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5686401724815369},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.5649000406265259},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5478699207305908},{"id":"https://openalex.org/keywords/random-projection","display_name":"Random projection","score":0.5368978977203369},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.47508111596107483},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.4600524604320526},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.4315561056137085},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42136672139167786},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38101622462272644},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3363110423088074}],"concepts":[{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.9038381576538086},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6934264898300171},{"id":"https://openalex.org/C64452783","wikidata":"https://www.wikidata.org/wiki/Q1524945","display_name":"Ternary operation","level":2,"score":0.6426716446876526},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.6186935901641846},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5926569104194641},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5686401724815369},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.5649000406265259},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5478699207305908},{"id":"https://openalex.org/C2777036070","wikidata":"https://www.wikidata.org/wiki/Q18393452","display_name":"Random projection","level":2,"score":0.5368978977203369},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.47508111596107483},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.4600524604320526},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.4315561056137085},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42136672139167786},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38101622462272644},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3363110423088074},{"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/globalsip.2017.8309136","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2017.8309136","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1708.08311","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1708.08311","pdf_url":"https://arxiv.org/pdf/1708.08311","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"mag:2752185112","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1708.08311.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1708.08311","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1708.08311","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1708.08311","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1708.08311","pdf_url":"https://arxiv.org/pdf/1708.08311","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321730","display_name":"Fonds Wetenschappelijk Onderzoek","ror":"https://ror.org/03qtxy027"},{"id":"https://openalex.org/F4320322634","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2752185112.pdf","grobid_xml":"https://content.openalex.org/works/W2752185112.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W1630192938","https://openalex.org/W1712805121","https://openalex.org/W1829836923","https://openalex.org/W1885185971","https://openalex.org/W1902934009","https://openalex.org/W1985324531","https://openalex.org/W1986931325","https://openalex.org/W1998310695","https://openalex.org/W2030449718","https://openalex.org/W2110764733","https://openalex.org/W2117539524","https://openalex.org/W2119275775","https://openalex.org/W2121194215","https://openalex.org/W2129812098","https://openalex.org/W2132792959","https://openalex.org/W2134033146","https://openalex.org/W2135859872","https://openalex.org/W2145094598","https://openalex.org/W2145096794","https://openalex.org/W2156043924","https://openalex.org/W2219727625","https://openalex.org/W2296616510","https://openalex.org/W2300242332","https://openalex.org/W2402050027","https://openalex.org/W2507344106","https://openalex.org/W2517289061","https://openalex.org/W2949117887","https://openalex.org/W2963674932","https://openalex.org/W2964121744","https://openalex.org/W2964299589","https://openalex.org/W3102722370","https://openalex.org/W4250955649","https://openalex.org/W6631190155","https://openalex.org/W6638632666","https://openalex.org/W6638667902","https://openalex.org/W6639703010","https://openalex.org/W6677580257","https://openalex.org/W6679164396","https://openalex.org/W6679574507","https://openalex.org/W6681096077","https://openalex.org/W6682889407"],"related_works":["https://openalex.org/W2964315752","https://openalex.org/W3113075849","https://openalex.org/W1481995007","https://openalex.org/W2474424652","https://openalex.org/W2004837810","https://openalex.org/W1565317672","https://openalex.org/W2744780915","https://openalex.org/W2965405987","https://openalex.org/W2963081547","https://openalex.org/W1544163959","https://openalex.org/W2089660191","https://openalex.org/W2124698503","https://openalex.org/W3129002116","https://openalex.org/W2947068802","https://openalex.org/W2946877649","https://openalex.org/W2198678136","https://openalex.org/W2592293938","https://openalex.org/W3005946484","https://openalex.org/W2147361252","https://openalex.org/W2595310496"],"abstract_inverted_index":{"Compressed":[0],"sensing":[1,24],"(CS)":[2],"is":[3,35],"a":[4,23,67,86,89,93],"sampling":[5],"theory":[6,34],"that":[7],"allows":[8],"reconstruction":[9,94],"of":[10,19,22,88,109,122],"sparse":[11,50,74],"(or":[12],"compressible)":[13],"signals":[14],"from":[15],"an":[16,28,97],"incomplete":[17],"number":[18],"measurements,":[20],"using":[21],"mechanism":[25],"implemented":[26],"by":[27],"appropriate":[29],"projection":[30,40,90],"matrix.":[31],"The":[32,100],"CS":[33],"based":[36],"on":[37,103],"random":[38],"Gaussian":[39],"matrices,":[41],"which":[42],"satisfy":[43],"recovery":[44],"guarantees":[45],"with":[46,117],"high":[47],"probability;":[48],"however,":[49],"ternary":[51,75],"{0,":[52],"-1,":[53],"+1}":[54],"projections":[55,76],"are":[56],"more":[57],"suitable":[58],"for":[59,77],"hardware":[60],"implementation.":[61],"In":[62],"this":[63],"paper,":[64],"we":[65],"present":[66],"deep":[68,81],"learning":[69,82],"approach":[70,112],"to":[71,114],"obtain":[72],"very":[73],"compressed":[78],"sensing.":[79],"Our":[80],"architecture":[83],"jointly":[84],"learns":[85],"pair":[87],"matrix":[91],"and":[92],"operator":[95],"in":[96,120],"end-to-end":[98],"fashion.":[99],"experimental":[101],"results":[102],"real":[104],"images":[105],"demonstrate":[106],"the":[107,110],"effectiveness":[108],"proposed":[111],"compared":[113],"state-of-the-art":[115],"methods,":[116],"significant":[118],"advantage":[119],"terms":[121],"complexity.":[123]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}