{"id":"https://openalex.org/W4206252293","doi":"https://doi.org/10.1109/access.2021.3137656","title":"Bayesian Approach in a Learning-Based Hyperspectral Image Denoising Framework","display_name":"Bayesian Approach in a Learning-Based Hyperspectral Image Denoising Framework","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W4206252293","doi":"https://doi.org/10.1109/access.2021.3137656"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3137656","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3137656","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09658545.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09658545.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012329610","display_name":"Hazique Aetesam","orcid":"https://orcid.org/0000-0003-3297-9949"},"institutions":[{"id":"https://openalex.org/I132153292","display_name":"Indian Institute of Technology Patna","ror":"https://ror.org/01ft5vz71","country_code":"IN","type":"education","lineage":["https://openalex.org/I132153292"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Hazique Aetesam","raw_affiliation_strings":["Dept. of Computer Science and Engineering, Indian Institute of Technology, Patna-801106, India. (e-mail: hazique1234@gmail.com)","IITP - Indian Institute of Technology Patna (Bihta, Patna -801106 (Bihar) - Inde)"],"raw_orcid":"https://orcid.org/0000-0003-3297-9949","affiliations":[{"raw_affiliation_string":"Dept. of Computer Science and Engineering, Indian Institute of Technology, Patna-801106, India. (e-mail: hazique1234@gmail.com)","institution_ids":["https://openalex.org/I132153292"]},{"raw_affiliation_string":"IITP - Indian Institute of Technology Patna (Bihta, Patna -801106 (Bihar) - Inde)","institution_ids":["https://openalex.org/I132153292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002377642","display_name":"Suman Kumar Maji","orcid":"https://orcid.org/0000-0003-4019-0980"},"institutions":[{"id":"https://openalex.org/I132153292","display_name":"Indian Institute of Technology Patna","ror":"https://ror.org/01ft5vz71","country_code":"IN","type":"education","lineage":["https://openalex.org/I132153292"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Suman Kumar Maji","raw_affiliation_strings":["Dept. of Computer Science and Engineering, Indian Institute of Technology, Patna-801106, India","IITP - Indian Institute of Technology Patna (Bihta, Patna -801106 (Bihar) - Inde)"],"raw_orcid":"https://orcid.org/0000-0003-4019-0980","affiliations":[{"raw_affiliation_string":"Dept. of Computer Science and Engineering, Indian Institute of Technology, Patna-801106, India","institution_ids":["https://openalex.org/I132153292"]},{"raw_affiliation_string":"IITP - Indian Institute of Technology Patna (Bihta, Patna -801106 (Bihar) - Inde)","institution_ids":["https://openalex.org/I132153292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019299408","display_name":"Hussein Yahia","orcid":"https://orcid.org/0000-0002-4284-096X"},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Hussein Yahia","raw_affiliation_strings":["Geostat team of INRIA Bordeaux, 200 rue de la vieille tour, 33405 Talence Cedex, France","GeoStat - Geometry and Statistics in acquisition data (200, avenue de la Vieille Tour \r\n33405 Talence cedex - France)"],"raw_orcid":"https://orcid.org/0000-0002-4284-096X","affiliations":[{"raw_affiliation_string":"Geostat team of INRIA Bordeaux, 200 rue de la vieille tour, 33405 Talence Cedex, France","institution_ids":["https://openalex.org/I1326498283"]},{"raw_affiliation_string":"GeoStat - Geometry and Statistics in acquisition data (200, avenue de la Vieille Tour \r\n33405 Talence cedex - France)","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1750,"currency":"USD","value_usd":1750},"apc_paid":{"value":775,"currency":"EUR","value_usd":835},"fwci":1.2049,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.79376691,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"9","issue":null,"first_page":"169335","last_page":"169347"},"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":0.9997000098228455,"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":0.9993000030517578,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7167987823486328},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6976869702339172},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6693323254585266},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6291444301605225},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6074482798576355},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5868868231773376},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5863500833511353},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5670276880264282},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.5518612265586853},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.43726491928100586},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.43422606587409973},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4257199466228485},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.41353335976600647},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20677965879440308},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.20551970601081848},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1962488293647766},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0908035933971405}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7167987823486328},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6976869702339172},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6693323254585266},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6291444301605225},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6074482798576355},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5868868231773376},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5863500833511353},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5670276880264282},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.5518612265586853},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.43726491928100586},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.43422606587409973},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4257199466228485},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.41353335976600647},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20677965879440308},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.20551970601081848},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1962488293647766},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0908035933971405},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2021.3137656","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3137656","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09658545.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:HAL:hal-03502378v1","is_oa":false,"landing_page_url":"https://inria.hal.science/hal-03502378","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, 2021, pp.1-1. &#x27E8;10.1109/ACCESS.2021.3137656&#x27E9;","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:ebce0581afb14458beeead7da1fd3099","is_oa":true,"landing_page_url":"https://doaj.org/article/ebce0581afb14458beeead7da1fd3099","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":"IEEE Access, Vol 9, Pp 169335-169347 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3137656","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3137656","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09658545.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.75}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4206252293.pdf","grobid_xml":"https://content.openalex.org/works/W4206252293.grobid-xml"},"referenced_works_count":74,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1836465849","https://openalex.org/W1915360731","https://openalex.org/W1915485278","https://openalex.org/W1944540851","https://openalex.org/W1969352102","https://openalex.org/W1980849288","https://openalex.org/W1988386267","https://openalex.org/W1991003630","https://openalex.org/W1994040806","https://openalex.org/W2000550835","https://openalex.org/W2034293618","https://openalex.org/W2050497921","https://openalex.org/W2053134012","https://openalex.org/W2053514113","https://openalex.org/W2058300393","https://openalex.org/W2082590963","https://openalex.org/W2100109944","https://openalex.org/W2118103795","https://openalex.org/W2133665775","https://openalex.org/W2136210095","https://openalex.org/W2150427434","https://openalex.org/W2160484748","https://openalex.org/W2172275395","https://openalex.org/W2184334976","https://openalex.org/W2289756263","https://openalex.org/W2326587421","https://openalex.org/W2334911726","https://openalex.org/W2346728112","https://openalex.org/W2399581072","https://openalex.org/W2471801048","https://openalex.org/W2520430674","https://openalex.org/W2525747617","https://openalex.org/W2528170672","https://openalex.org/W2544238497","https://openalex.org/W2574952845","https://openalex.org/W2585357012","https://openalex.org/W2588610957","https://openalex.org/W2593128366","https://openalex.org/W2613155248","https://openalex.org/W2614611154","https://openalex.org/W2748186179","https://openalex.org/W2753754894","https://openalex.org/W2766199454","https://openalex.org/W2773415061","https://openalex.org/W2790528326","https://openalex.org/W2806155925","https://openalex.org/W2914736033","https://openalex.org/W2962747489","https://openalex.org/W2963213461","https://openalex.org/W2963840672","https://openalex.org/W2964179170","https://openalex.org/W2991209609","https://openalex.org/W2997123853","https://openalex.org/W2998841120","https://openalex.org/W3011223715","https://openalex.org/W3021083191","https://openalex.org/W3091981156","https://openalex.org/W3098435832","https://openalex.org/W3103919952","https://openalex.org/W3147296415","https://openalex.org/W3167568784","https://openalex.org/W4242059867","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6677645113","https://openalex.org/W6696085341","https://openalex.org/W6704980600","https://openalex.org/W6720275808","https://openalex.org/W6727116172","https://openalex.org/W6733590821","https://openalex.org/W6743861043","https://openalex.org/W6782655014","https://openalex.org/W6792818011"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W2146049072","https://openalex.org/W1839961359","https://openalex.org/W2118877323","https://openalex.org/W2075146114","https://openalex.org/W2143264198","https://openalex.org/W2100805585"],"abstract_inverted_index":{"Hyperspectral":[0],"images":[1],"are":[2,62],"corrupted":[3,142],"by":[4,29],"a":[5,23,58,77,117,156],"combination":[6],"of":[7,16,37,105,151,159],"Gaussian-impulse":[8],"noise.":[9],"On":[10,45],"one":[11],"hand,":[12,48],"the":[13,18,30,46,49,66,85,99,103,132,148,152],"traditional":[14],"approach":[15],"handling":[17],"denoising":[19,133],"problem":[20],"using":[21],"maximum":[22],"posteriori":[24],"criterion":[25],"is":[26],"often":[27,89],"restricted":[28],"time-consuming":[31],"iterative":[32],"optimization":[33,114],"process":[34,88],"and":[35,125,137,143,161],"design":[36,98],"hand-crafted":[38],"priors":[39],"to":[40,65,91,109],"obtain":[41],"an":[42],"optimal":[43],"result.":[44],"other":[47],"discriminative":[50,73],"learning-based":[51],"approaches":[52],"offer":[53],"fast":[54],"inference":[55],"speed":[56],"over":[57],"trained":[59,75],"model;":[60],"but":[61],"highly":[63],"sensitive":[64],"noise":[67],"level":[68],"used":[69],"for":[70],"training.":[71],"A":[72],"model":[74],"with":[76,84],"loss":[78,106,119],"function":[79],"which":[80],"does":[81],"not":[82],"accord":[83],"Bayesian":[86,123],"degradation":[87],"leads":[90],"sub-optimal":[92],"results.":[93],"In":[94],"this":[95],"paper,":[96],"we":[97],"training":[100,130],"paradigm":[101],"emphasizing":[102],"role":[104],"functions;":[107],"similar":[108],"as":[110],"observed":[111],"in":[112,122,127],"model-based":[113],"methods.":[115],"As":[116],"result;":[118],"functions":[120],"derived":[121],"setting":[124],"employed":[126],"neural":[128],"network":[129],"boosts":[131],"performance.":[134],"Extensive":[135],"analysis":[136],"experimental":[138],"results":[139],"on":[140],"synthetically":[141],"real":[144],"hyperspectral":[145],"dataset":[146],"suggest":[147],"potential":[149],"applicability":[150],"proposed":[153],"technique":[154],"under":[155],"wide":[157],"range":[158],"homogeneous":[160],"heterogeneous":[162],"noisy":[163],"settings.":[164]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2025-10-10T00:00:00"}
