{"id":"https://openalex.org/W4303940471","doi":"https://doi.org/10.1186/s13634-022-00901-3","title":"Hyperspectral image denoising and destriping based on sparse representation, graph Laplacian regularization and stripe low-rank property","display_name":"Hyperspectral image denoising and destriping based on sparse representation, graph Laplacian regularization and stripe low-rank property","publication_year":2022,"publication_date":"2022-10-09","ids":{"openalex":"https://openalex.org/W4303940471","doi":"https://doi.org/10.1186/s13634-022-00901-3"},"language":"en","primary_location":{"id":"doi:10.1186/s13634-022-00901-3","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-022-00901-3","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-022-00901-3","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-022-00901-3","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100749618","display_name":"Zhi Zhang","orcid":"https://orcid.org/0009-0000-5180-0452"},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhi Zhang","raw_affiliation_strings":["Engineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Engineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China","institution_ids":["https://openalex.org/I43922553"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083483870","display_name":"Fang Yang","orcid":"https://orcid.org/0000-0002-8662-9998"},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Yang","raw_affiliation_strings":["Engineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China"],"raw_orcid":"https://orcid.org/0000-0002-8662-9998","affiliations":[{"raw_affiliation_string":"Engineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China","institution_ids":["https://openalex.org/I43922553"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100749618"],"corresponding_institution_ids":["https://openalex.org/I43922553"],"apc_list":{"value":1140,"currency":"GBP","value_usd":1398},"apc_paid":{"value":1140,"currency":"GBP","value_usd":1398},"fwci":0.4059,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.60168193,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"2022","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9998000264167786,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9987999796867371,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.7607134580612183},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.727178692817688},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.6646521091461182},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6577790975570679},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5918121337890625},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5800117254257202},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.5461410880088806},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5363209843635559},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5278646349906921},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.5061716437339783},{"id":"https://openalex.org/keywords/laplace-operator","display_name":"Laplace operator","score":0.4959748685359955},{"id":"https://openalex.org/keywords/dense-graph","display_name":"Dense graph","score":0.4957350194454193},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48749613761901855},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46671101450920105},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4120437502861023},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.39918750524520874},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3814726769924164},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.360767662525177},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.12834817171096802}],"concepts":[{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.7607134580612183},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.727178692817688},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.6646521091461182},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6577790975570679},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5918121337890625},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5800117254257202},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.5461410880088806},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5363209843635559},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5278646349906921},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.5061716437339783},{"id":"https://openalex.org/C165700671","wikidata":"https://www.wikidata.org/wiki/Q203484","display_name":"Laplace operator","level":2,"score":0.4959748685359955},{"id":"https://openalex.org/C13251829","wikidata":"https://www.wikidata.org/wiki/Q3085841","display_name":"Dense graph","level":5,"score":0.4957350194454193},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48749613761901855},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46671101450920105},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4120437502861023},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.39918750524520874},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3814726769924164},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.360767662525177},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.12834817171096802},{"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/C102192266","wikidata":"https://www.wikidata.org/wiki/Q4545823","display_name":"1-planar graph","level":4,"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/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s13634-022-00901-3","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-022-00901-3","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-022-00901-3","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:686d997b02634ed6942b6342a5886871","is_oa":true,"landing_page_url":"https://doaj.org/article/686d997b02634ed6942b6342a5886871","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EURASIP Journal on Advances in Signal Processing, Vol 2022, Iss 1, Pp 1-18 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s13634-022-00901-3","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-022-00901-3","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-022-00901-3","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6000000238418579,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G1046376829","display_name":null,"funder_award_id":"62101392","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4303940471.pdf","grobid_xml":"https://content.openalex.org/works/W4303940471.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1873031189","https://openalex.org/W1965221572","https://openalex.org/W1971524069","https://openalex.org/W1974438823","https://openalex.org/W1991003630","https://openalex.org/W1994040806","https://openalex.org/W1999174140","https://openalex.org/W2002177959","https://openalex.org/W2039596145","https://openalex.org/W2053514113","https://openalex.org/W2056370875","https://openalex.org/W2059745395","https://openalex.org/W2072026894","https://openalex.org/W2087263574","https://openalex.org/W2103972604","https://openalex.org/W2105884433","https://openalex.org/W2107799335","https://openalex.org/W2108119513","https://openalex.org/W2114122776","https://openalex.org/W2136625467","https://openalex.org/W2144359569","https://openalex.org/W2153663612","https://openalex.org/W2163886442","https://openalex.org/W2571662414","https://openalex.org/W2735711969","https://openalex.org/W2766804299","https://openalex.org/W2790888198","https://openalex.org/W2793775875","https://openalex.org/W2796431263","https://openalex.org/W2806155925","https://openalex.org/W2956143864","https://openalex.org/W2991209609","https://openalex.org/W3092436149","https://openalex.org/W3098435832","https://openalex.org/W3111299748","https://openalex.org/W3133845225","https://openalex.org/W3133902755","https://openalex.org/W3137135404","https://openalex.org/W3140885850"],"related_works":["https://openalex.org/W2091883426","https://openalex.org/W3173235360","https://openalex.org/W2174948646","https://openalex.org/W2024017047","https://openalex.org/W4318256793","https://openalex.org/W2051410394","https://openalex.org/W2594370889","https://openalex.org/W2390720471","https://openalex.org/W1600789676","https://openalex.org/W2134327945"],"abstract_inverted_index":{"Abstract":[0],"During":[1],"the":[2,20,37,48,69,76,94,99,102,112,122,146,149],"acquisition":[3],"of":[4,16,68,72,101,116,135,148],"a":[5,56],"hyperspectral":[6],"image":[7],"(HSI),":[8],"it":[9],"is":[10],"easily":[11],"corrupted":[12],"by":[13,65],"many":[14],"kinds":[15],"noises,":[17],"which":[18],"limits":[19],"subsequent":[21],"applications.":[22],"For":[23],"decades,":[24],"numerous":[25],"HSI":[26,64,153],"denoising":[27],"methods":[28,34],"have":[29],"been":[30],"proposed.":[31],"However,":[32],"these":[33],"rarely":[35],"consider":[36],"stripe":[38,49,73,127],"noise":[39,58,128],"as":[40],"an":[41,63,117],"independent":[42],"component,":[43],"thus":[44],"cannot":[45],"effectively":[46],"remove":[47,87,132],"noise.":[50,74,91],"In":[51,75],"this":[52],"paper,":[53],"we":[54],"propose":[55],"mixed":[57],"removal":[59],"algorithm":[60],"to":[61,86],"destripe":[62],"taking":[66],"advantage":[67],"low-rank":[70],"property":[71],"meantime,":[77],"sparse":[78,90,95,123],"representation":[79,96],"and":[80,89,126,142],"graph":[81,106],"Laplacian":[82,107],"regularization":[83,108],"are":[84],"utilized":[85],"Gaussian":[88],"Roughly":[92],"speaking,":[93],"helps":[97],"achieve":[98],"approximation":[100],"original":[103],"image.":[104],"A":[105],"term":[109],"can":[110,130],"ensure":[111],"non-local":[113],"spatial":[114],"similarity":[115],"HSI.":[118],"Separate":[119],"constraints":[120],"on":[121,139],"coefficient":[124],"matrix":[125],"components":[129],"help":[131],"different":[133],"types":[134],"noises.":[136],"Experimental":[137],"results":[138],"both":[140],"simulated":[141],"real":[143],"data":[144],"demonstrate":[145],"effectiveness":[147],"proposed":[150],"method":[151],"for":[152],"restoration.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}