{"id":"https://openalex.org/W2889043082","doi":"https://doi.org/10.1109/ssp.2018.8450712","title":"A Convex Low-Rank Regularization Method for Hyperspectral Super-Resolution","display_name":"A Convex Low-Rank Regularization Method for Hyperspectral Super-Resolution","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2889043082","doi":"https://doi.org/10.1109/ssp.2018.8450712","mag":"2889043082"},"language":"en","primary_location":{"id":"doi:10.1109/ssp.2018.8450712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2018.8450712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","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/A5057100970","display_name":"Ruiyuan Wu","orcid":"https://orcid.org/0000-0002-7751-4355"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruiyuan Wu","raw_affiliation_strings":["Department of Electronic Eng., The Chinese University of Hong Kong, Hong Kong SAR, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Eng., The Chinese University of Hong Kong, Hong Kong SAR, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100429992","display_name":"Qiang Li","orcid":"https://orcid.org/0000-0002-6736-3389"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Li","raw_affiliation_strings":["Department of Electronic Eng., The Chinese University of Hong Kong, Hong Kong SAR, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Eng., The Chinese University of Hong Kong, Hong Kong SAR, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015075381","display_name":"Xiao Fu","orcid":"https://orcid.org/0000-0003-4847-9586"},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiao Fu","raw_affiliation_strings":["School of Elec. Eng. & Comput. Sci., Oregon State University, OR, USA"],"affiliations":[{"raw_affiliation_string":"School of Elec. Eng. & Comput. Sci., Oregon State University, OR, USA","institution_ids":["https://openalex.org/I131249849"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016986549","display_name":"Wing\u2010Kin Ma","orcid":"https://orcid.org/0000-0001-7314-3537"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wing-Kin Ma","raw_affiliation_strings":["Department of Electronic Eng., The Chinese University of Hong Kong, Hong Kong SAR, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Eng., The Chinese University of Hong Kong, Hong Kong SAR, China","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5057100970"],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":null,"apc_paid":null,"fwci":0.2057,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.57727643,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"6","issue":null,"first_page":"383","last_page":"387"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8910921812057495},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.599461555480957},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5748980045318604},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5657715797424316},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.5641531348228455},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.5449357032775879},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5054864883422852},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4837510585784912},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4720093905925751},{"id":"https://openalex.org/keywords/matrix-completion","display_name":"Matrix completion","score":0.4561857283115387},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4520055651664734},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.4358220398426056},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.42832398414611816},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.36053961515426636},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3223534822463989},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31829744577407837},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.17425045371055603},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.08239328861236572}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8910921812057495},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.599461555480957},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5748980045318604},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5657715797424316},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.5641531348228455},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.5449357032775879},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5054864883422852},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4837510585784912},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4720093905925751},{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.4561857283115387},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4520055651664734},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.4358220398426056},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.42832398414611816},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.36053961515426636},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3223534822463989},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31829744577407837},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.17425045371055603},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.08239328861236572},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssp.2018.8450712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2018.8450712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W54434497","https://openalex.org/W653924242","https://openalex.org/W1608180792","https://openalex.org/W1971335521","https://openalex.org/W1981105828","https://openalex.org/W1990231296","https://openalex.org/W2010265430","https://openalex.org/W2021046129","https://openalex.org/W2055225600","https://openalex.org/W2063790512","https://openalex.org/W2087263574","https://openalex.org/W2100556411","https://openalex.org/W2117706739","https://openalex.org/W2118550318","https://openalex.org/W2119449648","https://openalex.org/W2144948131","https://openalex.org/W2149414429","https://openalex.org/W2311103336","https://openalex.org/W2323104748","https://openalex.org/W2339666411","https://openalex.org/W2343120142","https://openalex.org/W2625894731","https://openalex.org/W2800538536","https://openalex.org/W2913535645","https://openalex.org/W3102912004","https://openalex.org/W3104960002","https://openalex.org/W3141595720","https://openalex.org/W4244393449","https://openalex.org/W4285719527","https://openalex.org/W4301109526","https://openalex.org/W6602248423","https://openalex.org/W6621334021","https://openalex.org/W6642991451","https://openalex.org/W6698444068","https://openalex.org/W6703697878","https://openalex.org/W6750786094"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2022304901","https://openalex.org/W2018850895","https://openalex.org/W2988577871","https://openalex.org/W1987483041","https://openalex.org/W2758145160","https://openalex.org/W4205174160","https://openalex.org/W3103289951","https://openalex.org/W2912837894"],"abstract_inverted_index":{"Hyperspectral":[0],"super-resolution":[1,9],"(HSR)":[2],"is":[3,37,108,125],"a":[4,8,12,24,91,128],"technique":[5],"of":[6,64,76,137,148],"recovering":[7],"image":[10,14,26],"from":[11],"hyperspectral":[13],"(which":[15,27],"has":[16,28],"low":[17,32,65,96],"spatial":[18,30],"but":[19,31,121],"high":[20,29],"spectral":[21,33],"resolutions)":[22],"and":[23,44,49,100,139],"multispectral":[25],"resolutions).":[34],"The":[35],"problem":[36,41],"an":[38],"ill-posed":[39],"inverse":[40],"in":[42,72],"general,":[43],"thus":[45],"judiciously":[46],"designed":[47],"formulations":[48,102],"algorithms":[50],"are":[51,103,142],"needed":[52],"for":[53,94],"good":[54],"HSR":[55],"performance.":[56],"In":[57],"this":[58],"work,":[59],"we":[60,87],"employ":[61],"the":[62,79,105,111,117,135,146,149],"idea":[63],"rank":[66],"modeling,":[67],"which":[68,133],"was":[69],"proven":[70],"effective":[71],"helping":[73],"enhance":[74],"performance":[75],"HSR.":[77],"Unlike":[78],"extensively":[80],"employed":[81,143],"nonconvex":[82],"structured":[83],"matrix":[84],"factorization-based":[85],"methods,":[86],"propose":[88],"to":[89,144],"use":[90],"convex":[92],"regularizer":[93],"promoting":[95],"rank.":[97],"Both":[98],"unconstrained":[99,106],"constrained":[101,123],"considered:":[104],"case":[107,124],"tackled":[109],"by":[110,127],"proximal":[112],"gradient":[113],"(PG)":[114],"algorithm;":[115],"while":[116],"more":[118],"physically":[119],"sound":[120],"challenging":[122],"solved":[126],"custom-designed":[129],"PG":[130],"like":[131],"algorithm,":[132],"uses":[134],"ideas":[136],"smoothing":[138],"majorization-minimization.":[140],"Simulations":[141],"showcase":[145],"effectiveness":[147],"proposed":[150],"methods.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
