{"id":"https://openalex.org/W2792700572","doi":"https://doi.org/10.1109/camsap.2017.8313122","title":"A constrained formulation for compressive spectral image reconstruction using linear mixture models","display_name":"A constrained formulation for compressive spectral image reconstruction using linear mixture models","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2792700572","doi":"https://doi.org/10.1109/camsap.2017.8313122","mag":"2792700572"},"language":"en","primary_location":{"id":"doi:10.1109/camsap.2017.8313122","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap.2017.8313122","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","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/A5084685753","display_name":"Jorge Bacca","orcid":"https://orcid.org/0000-0001-5264-7891"},"institutions":[{"id":"https://openalex.org/I115684694","display_name":"Industrial University of Santander","ror":"https://ror.org/00xc1d948","country_code":"CO","type":"education","lineage":["https://openalex.org/I115684694"]}],"countries":["CO"],"is_corresponding":true,"raw_author_name":"Jorge Bacca","raw_affiliation_strings":["Department of Computer Science, Universidad Industrial de Santander, Bucaramanga, Colombia"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Universidad Industrial de Santander, Bucaramanga, Colombia","institution_ids":["https://openalex.org/I115684694"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079739747","display_name":"H\u00e9ctor Vargas","orcid":"https://orcid.org/0000-0001-7713-7708"},"institutions":[{"id":"https://openalex.org/I115684694","display_name":"Industrial University of Santander","ror":"https://ror.org/00xc1d948","country_code":"CO","type":"education","lineage":["https://openalex.org/I115684694"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Hector Vargas","raw_affiliation_strings":["Department of Electrical Engeneering, Universidad Industrial de Santander, Bucaramanga, Colombia"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engeneering, Universidad Industrial de Santander, Bucaramanga, Colombia","institution_ids":["https://openalex.org/I115684694"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081714132","display_name":"Henry Arg\u00fcello","orcid":"https://orcid.org/0000-0002-2202-253X"},"institutions":[{"id":"https://openalex.org/I115684694","display_name":"Industrial University of Santander","ror":"https://ror.org/00xc1d948","country_code":"CO","type":"education","lineage":["https://openalex.org/I115684694"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Henry Arguello","raw_affiliation_strings":["Department of Computer Science, Universidad Industrial de Santander, Bucaramanga, Colombia"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Universidad Industrial de Santander, Bucaramanga, Colombia","institution_ids":["https://openalex.org/I115684694"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5084685753"],"corresponding_institution_ids":["https://openalex.org/I115684694"],"apc_list":null,"apc_paid":null,"fwci":1.4406,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.80944741,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"231","issue":null,"first_page":"1","last_page":"5"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9995999932289124,"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9988999962806702,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9748517870903015},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.7007338404655457},{"id":"https://openalex.org/keywords/data-cube","display_name":"Data cube","score":0.6976391077041626},{"id":"https://openalex.org/keywords/full-spectral-imaging","display_name":"Full spectral imaging","score":0.6413144469261169},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.6041284203529358},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5940306782722473},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.527000367641449},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.5223856568336487},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4983360767364502},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.46297046542167664},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.4562952518463135},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4130246341228485},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3594017028808594},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.341462641954422},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.10893583297729492}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9748517870903015},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7007338404655457},{"id":"https://openalex.org/C78168278","wikidata":"https://www.wikidata.org/wiki/Q5227269","display_name":"Data cube","level":2,"score":0.6976391077041626},{"id":"https://openalex.org/C78660771","wikidata":"https://www.wikidata.org/wiki/Q5508206","display_name":"Full spectral imaging","level":3,"score":0.6413144469261169},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.6041284203529358},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5940306782722473},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.527000367641449},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.5223856568336487},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4983360767364502},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.46297046542167664},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.4562952518463135},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4130246341228485},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3594017028808594},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.341462641954422},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.10893583297729492},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/camsap.2017.8313122","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap.2017.8313122","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1519283961","https://openalex.org/W1608180792","https://openalex.org/W1811873273","https://openalex.org/W1974882232","https://openalex.org/W1978333359","https://openalex.org/W2028349405","https://openalex.org/W2032944446","https://openalex.org/W2034198076","https://openalex.org/W2038577730","https://openalex.org/W2056672982","https://openalex.org/W2056804312","https://openalex.org/W2074561958","https://openalex.org/W2083987637","https://openalex.org/W2084591647","https://openalex.org/W2109449402","https://openalex.org/W2119667497","https://openalex.org/W2120991191","https://openalex.org/W2121338139","https://openalex.org/W2122548617","https://openalex.org/W2150695437","https://openalex.org/W2151640445","https://openalex.org/W2164278908","https://openalex.org/W3102095506","https://openalex.org/W4285719527","https://openalex.org/W4292363360","https://openalex.org/W6621334021"],"related_works":["https://openalex.org/W4386427838","https://openalex.org/W2889956472","https://openalex.org/W2911259277","https://openalex.org/W2908721991","https://openalex.org/W3178760882","https://openalex.org/W1982418987","https://openalex.org/W1556234160","https://openalex.org/W1539874145","https://openalex.org/W2039721451","https://openalex.org/W2053822900"],"abstract_inverted_index":{"Recent":[0],"hyperspectral":[1,22,33,40,48,95],"imaging":[2,23,34],"systems":[3],"are":[4,37,50],"constructed":[5],"on":[6,133],"the":[7,16,86,94,115,125,143],"idea":[8],"of":[9,65,119,124],"compressive":[10,21,32],"sensing":[11],"for":[12,39],"efficient":[13],"acquisition.":[14],"However,":[15],"traditional":[17],"reconstruction":[18,41,58,73,145],"model":[19,59],"in":[20,42,62,130,137,141],"has":[24],"a":[25,43,53,63,78,134],"high":[26],"computational":[27],"complexity.":[28],"In":[29],"this":[30],"work,":[31],"and":[35,88],"unmixing":[36],"combined":[38],"low-complexity":[44],"scheme.":[45],"The":[46,57,99,121],"compressed":[47],"measurements":[49],"acquired":[51],"with":[52,83,114],"single":[54],"pixel":[55],"spectrometer.":[56],"is":[60,74,104,128],"represented":[61],"space":[64],"lower":[66],"dimension":[67],"named":[68],"linear":[69],"mixture":[70],"model.":[71],"Hyperspectral":[72],"then":[75],"formulated":[76],"as":[77],"nonnegative":[79,100],"matrix":[80,101],"factorization":[81,102],"problem":[82,103],"respect":[84],"to":[85],"endmembers":[87],"abundances,":[89],"bypassing":[90],"high-complexity":[91],"tasks":[92],"involving":[93],"data":[96,139],"cube":[97],"itself.":[98],"solved":[105],"by":[106],"combining":[107],"an":[108],"alternating":[109,116],"least-squares":[110],"based":[111],"estimation":[112],"strategy":[113],"direction":[117],"method":[118],"multipliers.":[120],"estimated":[122],"performance":[123],"proposed":[126],"scheme":[127],"illustrated":[129],"experiments":[131],"conducted":[132],"simulated":[135],"acquisition":[136],"real":[138],"outperforming":[140],"3dB":[142],"state-of-the-art":[144],"algorithms.":[146]},"counts_by_year":[{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}