{"id":"https://openalex.org/W4372271326","doi":"https://doi.org/10.1109/icassp49357.2023.10095911","title":"Enhancing Spatio-Spectral Regularization by Structure Tensor Modeling for Hyperspectral Image Denoising","display_name":"Enhancing Spatio-Spectral Regularization by Structure Tensor Modeling for Hyperspectral Image Denoising","publication_year":2023,"publication_date":"2023-05-05","ids":{"openalex":"https://openalex.org/W4372271326","doi":"https://doi.org/10.1109/icassp49357.2023.10095911"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49357.2023.10095911","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10095911","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5076185636","display_name":"Shingo Takemoto","orcid":"https://orcid.org/0000-0002-8075-8983"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Shingo Takemoto","raw_affiliation_strings":["Tokyo Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074225463","display_name":"Shunsuke Ono","orcid":null},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shunsuke Ono","raw_affiliation_strings":["Tokyo Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology","institution_ids":["https://openalex.org/I114531698"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5076185636"],"corresponding_institution_ids":["https://openalex.org/I114531698"],"apc_list":null,"apc_paid":null,"fwci":0.9772,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.77148494,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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":0.9998000264167786,"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":0.9995999932289124,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.7039622068405151},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.695382297039032},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.662534236907959},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5480395555496216},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5372085571289062},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4899384379386902},{"id":"https://openalex.org/keywords/total-variation-denoising","display_name":"Total variation denoising","score":0.47067102789878845},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44232335686683655},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.41843628883361816},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4177263081073761},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35408511757850647},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09001141786575317}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.7039622068405151},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.695382297039032},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.662534236907959},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5480395555496216},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5372085571289062},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4899384379386902},{"id":"https://openalex.org/C207282899","wikidata":"https://www.wikidata.org/wiki/Q7828156","display_name":"Total variation denoising","level":3,"score":0.47067102789878845},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44232335686683655},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41843628883361816},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4177263081073761},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35408511757850647},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09001141786575317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49357.2023.10095911","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10095911","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W609694675","https://openalex.org/W1600877088","https://openalex.org/W1667895010","https://openalex.org/W1895161527","https://openalex.org/W1971358070","https://openalex.org/W1998991750","https://openalex.org/W2063790512","https://openalex.org/W2092663520","https://openalex.org/W2123031198","https://openalex.org/W2133665775","https://openalex.org/W2158449358","https://openalex.org/W2163886442","https://openalex.org/W2180180824","https://openalex.org/W2289756263","https://openalex.org/W2396831964","https://openalex.org/W2594827121","https://openalex.org/W2603834682","https://openalex.org/W2620976957","https://openalex.org/W2766918872","https://openalex.org/W2805465265","https://openalex.org/W2941677189","https://openalex.org/W2942454403","https://openalex.org/W2962770389","https://openalex.org/W2962863128","https://openalex.org/W2964179170","https://openalex.org/W3075397214","https://openalex.org/W3097354450","https://openalex.org/W3098388691","https://openalex.org/W3131515870","https://openalex.org/W3199351457","https://openalex.org/W4233367343","https://openalex.org/W4286377574","https://openalex.org/W4394007378"],"related_works":["https://openalex.org/W3034655717","https://openalex.org/W2546645752","https://openalex.org/W3173596272","https://openalex.org/W2028628118","https://openalex.org/W2546871836","https://openalex.org/W2136485282","https://openalex.org/W2953317069","https://openalex.org/W4301408992","https://openalex.org/W1987739334","https://openalex.org/W2128028747"],"abstract_inverted_index":{"We":[0,143],"propose":[1],"a":[2,35,151,168],"new":[3],"regularization":[4,36,196],"function,":[5],"named":[6],"Spatio-Spectral":[7,22],"Structure":[8],"Tensor":[9],"Total":[10,23],"Variation":[11,24],"(S":[12],"<inf":[13,81,118,157,186],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[14,82,119,158,187],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">3</inf>":[15,83,120,159,188],"TTV),":[16],"for":[17,38],"hyperspectral":[18],"image":[19],"(HSI)":[20],"denoising.":[21],"(SSTV),":[25],"defined":[26,87],"using":[27],"spatio-spectral":[28,99,111],"second-order":[29,100],"differences,":[30],"is":[31,86],"widely":[32],"known":[33],"as":[34,110,150],"function":[37],"HSI":[39,147,195],"that":[40],"can":[41,63,122],"effectively":[42],"remove":[43],"noise":[44,72,200],"while":[45],"avoiding":[46],"spatial":[47,66,104,130],"over-smoothing.":[48],"However,":[49],"since":[50],"SSTV":[51],"only":[52,125],"refers":[53],"to":[54,174],"the":[55,69,89,92,126,137,146,182],"information":[56],"of":[57,71,91,95,98,128,184],"neighboring":[58],"pixels":[59],"or":[60],"bands,":[61],"it":[62,192],"corrupt":[64],"semi-local":[65,129],"structure":[67,112,131],"in":[68,102],"process":[70],"removal.":[73],"To":[74],"resolve":[75],"this":[76,115,177],"problem,":[77],"we":[78,180],"formulate":[79,145],"S":[80,117,156,185],"TTV,":[84],"which":[85],"by":[88,190],"sum":[90],"nuclear":[93],"norms":[94],"matrices":[96,109],"consisting":[97],"differences":[101],"small":[103],"blocks":[105],"(we":[106],"call":[107],"these":[108],"tensors).":[113],"With":[114],"formulation,":[116],"TTV":[121,160,189],"capture":[123],"not":[124],"similarity":[127],"between":[132],"adjacent":[133],"bands":[134],"but":[135],"also":[136,144],"spectral":[138],"correlation":[139],"across":[140],"all":[141],"bands.":[142],"denoising":[148],"problem":[149,154],"convex":[152],"optimization":[153],"involving":[155],"and":[161],"develop":[162],"an":[163],"efficient":[164],"algorithm":[165],"based":[166],"on":[167],"diagonally":[169],"preconditioned":[170],"primal-dual":[171],"splitting":[172],"method":[173],"efficiently":[175],"solve":[176],"problem.":[178],"Finally,":[179],"demonstrate":[181],"effectiveness":[183],"comparing":[191],"with":[193],"state-of-the-art":[194],"models":[197],"through":[198],"mixed":[199],"removal":[201],"experiments.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-14T08:43:22.919905","created_date":"2025-10-10T00:00:00"}
