{"id":"https://openalex.org/W4391164272","doi":"https://doi.org/10.1109/lgrs.2024.3357833","title":"A Remote Sensing Hyperspectral Image Noise Removal Method Based on Multipriors Guidance","display_name":"A Remote Sensing Hyperspectral Image Noise Removal Method Based on Multipriors Guidance","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4391164272","doi":"https://doi.org/10.1109/lgrs.2024.3357833"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2024.3357833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2024.3357833","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-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/A5003113155","display_name":"Yinhu Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinhu Wu","raw_affiliation_strings":["School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100696425","display_name":"Junping Zhang","orcid":"https://orcid.org/0000-0002-1082-114X"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junping Zhang","raw_affiliation_strings":["School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China"],"raw_orcid":"https://orcid.org/0000-0002-1082-114X","affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100761789","display_name":"Dongyang Liu","orcid":"https://orcid.org/0000-0003-3342-8356"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongyang Liu","raw_affiliation_strings":["School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China"],"raw_orcid":"https://orcid.org/0000-0003-3342-8356","affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":1.0846,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.78303325,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"21","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9993000030517578,"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":0.9993000030517578,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9980999827384949,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9976000189781189,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.905977725982666},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6368629932403564},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6337919235229492},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5522050857543945},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5082278251647949},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5032066702842712},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5006203651428223},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4579841196537018},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3280388116836548},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.17661044001579285},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.1309756636619568}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.905977725982666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6368629932403564},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6337919235229492},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5522050857543945},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5082278251647949},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5032066702842712},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5006203651428223},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4579841196537018},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3280388116836548},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.17661044001579285},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.1309756636619568}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2024.3357833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2024.3357833","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5291607210","display_name":null,"funder_award_id":"62271171","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2520632654","https://openalex.org/W2585357012","https://openalex.org/W2919868964","https://openalex.org/W2984522085","https://openalex.org/W3021083191","https://openalex.org/W3090929676","https://openalex.org/W3118496544","https://openalex.org/W3130388341","https://openalex.org/W3133902755","https://openalex.org/W3140885850","https://openalex.org/W3183780241","https://openalex.org/W3204653398","https://openalex.org/W3207918547","https://openalex.org/W4214543554","https://openalex.org/W4214902837","https://openalex.org/W4323644249","https://openalex.org/W4361290008","https://openalex.org/W4368232741"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W2060875994","https://openalex.org/W2027399350","https://openalex.org/W2044184146","https://openalex.org/W2385371209","https://openalex.org/W4250051149","https://openalex.org/W2083270190"],"abstract_inverted_index":{"Remote":[0],"sensing":[1],"hyperspectral":[2],"images":[3],"(HSIs)":[4],"have":[5],"been":[6],"applied":[7],"in":[8,153],"a":[9,78,133],"variety":[10],"of":[11,20,45,91,114,141],"fields.":[12],"However,":[13],"HSIs":[14,81],"are":[15,33],"susceptible":[16],"to":[17,122,149,172],"various":[18],"types":[19],"noise":[21,59,63,69,82,152],"which":[22],"affect":[23],"both":[24],"their":[25],"quality":[26],"and":[27,35,48,94,128,143,157,168],"subsequent":[28],"analysis.":[29],"Existing":[30],"knowledge-driven":[31],"methods":[32,41,54,93,97],"time-consuming":[34],"need":[36],"handcrafted":[37],"parameters,":[38],"while":[39],"data-driven":[40],"require":[42],"large":[43],"amounts":[44],"training":[46],"resources":[47],"lack":[49],"interpretability.":[50],"What\u2019s":[51],"more,":[52],"most":[53],"mainly":[55],"focus":[56],"on":[57,111],"Gaussian":[58],"rather":[60],"than":[61],"Poisson-Gaussian":[62,102],"that":[64,85,137,162],"matches":[65],"better":[66,166],"the":[67,89,101,112,115,124,139,151,154,173],"real":[68],"model.":[70],"To":[71],"address":[72],"this":[73,75],"issue,":[74],"paper":[76],"proposes":[77],"multi-priors":[79],"guided":[80],"removal":[83],"method":[84,164],"not":[86],"only":[87],"combines":[88],"benefits":[90],"traditional":[92],"deep":[95],"learning":[96],"but":[98],"also":[99],"considers":[100],"based":[103,110],"mixed":[104],"noise.":[105],"Specifically,":[106],"tensor":[107],"subspace":[108],"representation":[109],"guidance":[113,140],"global":[116],"spectral":[117,130],"low-rank":[118],"prior":[119],"is":[120,147],"employed":[121],"decompose":[123],"HSI":[125],"into":[126],"eigen-images":[127,155],"orthogonal":[129],"basis.":[131],"Then":[132],"nonlocal-local":[134],"aware":[135],"network":[136],"incorporates":[138],"local":[142],"nonlocal":[144],"self-similarity":[145],"priors":[146],"constructed":[148],"remove":[150],"effectively":[156],"efficiently.":[158],"Extensive":[159],"experiments":[160],"demonstrate":[161],"our":[163],"achieves":[165],"quantitative":[167],"qualitative":[169],"performance":[170],"compared":[171],"state-of-the-art":[174],"methods.":[175]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
