{"id":"https://openalex.org/W2772820061","doi":"https://doi.org/10.1109/igarss.2017.8128235","title":"Hyperspectral image denoising with multiscale low-rank matrix recovery","display_name":"Hyperspectral image denoising with multiscale low-rank matrix recovery","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2772820061","doi":"https://doi.org/10.1109/igarss.2017.8128235","mag":"2772820061"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2017.8128235","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8128235","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","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/A5063874284","display_name":"Zhihong Huang","orcid":"https://orcid.org/0000-0003-0549-1059"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhihong Huang","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067097659","display_name":"Shutao Li","orcid":"https://orcid.org/0000-0002-0585-9848"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shutao Li","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047031064","display_name":"Fang Chao Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Hu","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5063874284"],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":null,"apc_paid":null,"fwci":0.3641,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.699344,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"5442","last_page":"5445"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9995999932289124,"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":0.9977999925613403,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6901929378509521},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6641489863395691},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6609708070755005},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6553256511688232},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6162853837013245},{"id":"https://openalex.org/keywords/impulse-noise","display_name":"Impulse noise","score":0.5614026784896851},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5528721213340759},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5441185235977173},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.5176559090614319},{"id":"https://openalex.org/keywords/robust-principal-component-analysis","display_name":"Robust principal component analysis","score":0.5145449042320251},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.49462267756462097},{"id":"https://openalex.org/keywords/low-rank-approximation","display_name":"Low-rank approximation","score":0.47869113087654114},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4780500531196594},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.47081977128982544},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.40705209970474243},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21205255389213562},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.15062570571899414}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6901929378509521},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6641489863395691},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6609708070755005},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6553256511688232},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6162853837013245},{"id":"https://openalex.org/C127372701","wikidata":"https://www.wikidata.org/wiki/Q16979398","display_name":"Impulse noise","level":3,"score":0.5614026784896851},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5528721213340759},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5441185235977173},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.5176559090614319},{"id":"https://openalex.org/C2777749129","wikidata":"https://www.wikidata.org/wiki/Q17148469","display_name":"Robust principal component analysis","level":3,"score":0.5145449042320251},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49462267756462097},{"id":"https://openalex.org/C90199385","wikidata":"https://www.wikidata.org/wiki/Q6692777","display_name":"Low-rank approximation","level":3,"score":0.47869113087654114},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4780500531196594},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.47081977128982544},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40705209970474243},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21205255389213562},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.15062570571899414},{"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C25023664","wikidata":"https://www.wikidata.org/wiki/Q1575637","display_name":"Hankel matrix","level":2,"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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2017.8128235","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8128235","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","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":11,"referenced_works":["https://openalex.org/W1939429412","https://openalex.org/W1944540851","https://openalex.org/W1969352102","https://openalex.org/W1974438823","https://openalex.org/W1994040806","https://openalex.org/W2056370875","https://openalex.org/W2067983477","https://openalex.org/W2135431554","https://openalex.org/W2145962650","https://openalex.org/W2591248827","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2159720070","https://openalex.org/W2463830379","https://openalex.org/W1991133639","https://openalex.org/W2157134024","https://openalex.org/W2165264176","https://openalex.org/W2290527899","https://openalex.org/W2064270814","https://openalex.org/W2286538174","https://openalex.org/W2016732018","https://openalex.org/W2985488186"],"abstract_inverted_index":{"Recently,":[0],"low-rank":[1,20,43,113],"matrix":[2,21,44,114],"recovery":[3,22,45,115],"has":[4],"been":[5],"demonstrated":[6],"to":[7,49,82,87,130,137],"be":[8],"an":[9],"effective":[10],"tool":[11],"in":[12,117],"hyperspectral":[13],"images":[14],"(HSIs)":[15],"denoising.":[16],"However,":[17],"the":[18,28,70,83,89,96,112,153,156],"previous":[19],"method":[23,60],"with":[24],"a":[25,118,122,139],"window":[26],"of":[27,56,134,155],"fixed-shape":[29],"cannot":[30],"adaptively":[31],"exploit":[32],"spatial":[33],"structure":[34],"information":[35],"and":[36,76,146],"nonlocal":[37],"similarity.":[38],"In":[39],"this":[40],"paper,":[41],"multiscale":[42,77],"(MC-LRMR)":[46],"is":[47,67,80,128],"proposed":[48,59,157],"recover":[50],"HSI":[51,66,90,148],"corrupted":[52],"by":[53,69,111],"different":[54],"kinds":[55],"noise.":[57],"The":[58],"contains":[61],"three":[62],"main":[63],"steps.":[64],"First,":[65],"transformed":[68],"principal":[71,85],"component":[72],"analysis":[73],"(PCA)":[74],"algorithm":[75],"superpixel":[78],"segmentation":[79],"applied":[81],"first":[84],"component,":[86],"segment":[88],"into":[91],"non-overlapping":[92],"homogeneous":[93],"regions.":[94],"Then,":[95],"mixed":[97],"noises,":[98],"including":[99],"Gaussian":[100],"noise,":[101,103,106,108],"impulse":[102],"dead":[104],"lines":[105],"stripes":[107],"are":[109],"removed":[110],"(LRMR)":[116],"superpixel-by-superpixel":[119],"manner.":[120],"Finally,":[121],"fusion":[123],"rule,":[124],"i.e.,":[125],"average":[126],"operator,":[127],"adopted":[129],"combine":[131],"denoising":[132],"results":[133],"various":[135],"scales,":[136],"acquire":[138],"fused":[140],"noise-free":[141],"estimation.":[142],"Experiments":[143],"on":[144],"simulated":[145],"real":[147],"data":[149],"sets":[150],"can":[151],"demonstrate":[152],"effectiveness":[154],"method.":[158]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}