{"id":"https://openalex.org/W2517723848","doi":"https://doi.org/10.1109/icip.2016.7532691","title":"Direct inference on compressive measurements using convolutional neural networks","display_name":"Direct inference on compressive measurements using convolutional neural networks","publication_year":2016,"publication_date":"2016-08-17","ids":{"openalex":"https://openalex.org/W2517723848","doi":"https://doi.org/10.1109/icip.2016.7532691","mag":"2517723848"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2016.7532691","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532691","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 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/A5056968977","display_name":"Suhas Lohit","orcid":"https://orcid.org/0000-0002-0392-3818"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Suhas Lohit","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077902428","display_name":"Kuldeep Kulkarni","orcid":null},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kuldeep Kulkarni","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062945520","display_name":"Pavan Turaga","orcid":"https://orcid.org/0000-0002-5263-5943"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pavan Turaga","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5056968977"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":9.1268,"has_fulltext":false,"cited_by_count":113,"citation_normalized_percentile":{"value":0.98584262,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1913","last_page":"1917"},"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.998199999332428,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9957000017166138,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.9202666282653809},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.7956782579421997},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7847170829772949},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7406928539276123},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7202656865119934},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7046363353729248},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6994825601577759},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5582234859466553},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5188959240913391},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.49689653515815735},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4676040709018707},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37482666969299316},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3686278462409973}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.9202666282653809},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7956782579421997},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7847170829772949},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7406928539276123},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7202656865119934},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7046363353729248},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6994825601577759},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5582234859466553},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5188959240913391},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.49689653515815735},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4676040709018707},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37482666969299316},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3686278462409973}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2016.7532691","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532691","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.75,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1744937271","https://openalex.org/W1828462930","https://openalex.org/W1956120499","https://openalex.org/W1984584561","https://openalex.org/W1986931325","https://openalex.org/W2020807785","https://openalex.org/W2062747674","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2117539524","https://openalex.org/W2119667497","https://openalex.org/W2126131432","https://openalex.org/W2127271355","https://openalex.org/W2137945624","https://openalex.org/W2145287260","https://openalex.org/W2155893237","https://openalex.org/W2160172035","https://openalex.org/W2163605009","https://openalex.org/W2164452299","https://openalex.org/W2296616510","https://openalex.org/W2979473749","https://openalex.org/W3147600416","https://openalex.org/W4250955649","https://openalex.org/W6637654295","https://openalex.org/W6638642125","https://openalex.org/W6655513006","https://openalex.org/W6674914833","https://openalex.org/W6678689058","https://openalex.org/W6684191040","https://openalex.org/W6769341872"],"related_works":["https://openalex.org/W2950475743","https://openalex.org/W4386603768","https://openalex.org/W2886711096","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W2590796488","https://openalex.org/W4389249638","https://openalex.org/W2734358244","https://openalex.org/W4388700941","https://openalex.org/W2896778670"],"abstract_inverted_index":{"Compressive":[0,22],"imagers,":[1],"e.g.":[2],"the":[3,10,16,30,62,79,92],"single-pixel":[4],"camera":[5],"(SPC),":[6],"acquire":[7],"measurements":[8],"in":[9,41,73],"form":[11],"of":[12,15,19,29,37,64,78,159],"random":[13],"projections":[14],"scene":[17],"instead":[18],"pixel":[20],"intensities.":[21],"Sensing":[23],"(CS)":[24],"theory":[25],"allows":[26],"accurate":[27],"reconstruction":[28,44,59],"image":[31,143],"even":[32,154],"from":[33,91,119],"a":[34],"small":[35],"number":[36],"such":[38],"projections.":[39],"However,":[40],"practice,":[42],"most":[43],"algorithms":[45],"perform":[46],"poorly":[47],"at":[48,155],"low":[49,156],"measurement":[50,157],"rates":[51,158],"and":[52,142],"are":[53,71],"computationally":[54],"very":[55],"expensive.":[56],"But":[57],"perfect":[58],"is":[60,88,152],"not":[61],"goal":[63],"high-level":[65,129],"computer":[66],"vision":[67],"applications.":[68],"Instead,":[69],"we":[70,103,125,148],"interested":[72],"only":[74],"determining":[75],"certain":[76],"properties":[77],"image.":[80],"Recent":[81],"work":[82],"has":[83],"shown":[84],"that":[85,105,127,150],"effective":[86,128],"inference":[87,130],"possible":[89,153],"directly":[90,118],"compressive":[93],"measurements,":[94],"without":[95],"reconstruction,":[96],"using":[97,135],"correlational":[98],"features.":[99],"In":[100],"this":[101],"paper,":[102],"show":[104,149],"convolutional":[106],"neural":[107],"networks":[108],"(CNNs)":[109],"can":[110,131],"be":[111,132],"employed":[112],"to":[113],"extract":[114],"discriminative":[115],"non-linear":[116],"features":[117],"CS":[120],"measurements.":[121],"Using":[122],"these":[123],"features,":[124],"demonstrate":[126],"performed.":[133],"Experimentally,":[134],"hand":[136],"written":[137],"digit":[138],"recognition":[139,144,151],"(MNIST":[140],"dataset)":[141],"(ImageNet)":[145],"as":[146],"examples,":[147],"about":[160],"0.1.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":17},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
