{"id":"https://openalex.org/W4387968252","doi":"https://doi.org/10.1145/3581783.3612016","title":"Hyperspectral Image Denoising with Spectrum Alignment","display_name":"Hyperspectral Image Denoising with Spectrum Alignment","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4387968252","doi":"https://doi.org/10.1145/3581783.3612016"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3612016","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","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/A5011475525","display_name":"Jiahua Xiao","orcid":"https://orcid.org/0000-0002-0469-528X"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahua Xiao","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-0469-528X","affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080268898","display_name":"Yantao Ji","orcid":"https://orcid.org/0000-0002-4146-2661"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yantao Ji","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-4146-2661","affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040886197","display_name":"Xing Wei","orcid":"https://orcid.org/0000-0002-5025-3941"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Wei","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-5025-3941","affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6737,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.71901914,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5495","last_page":"5503"},"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":0.9972000122070312,"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":0.8928475975990295},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.7083485126495361},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6763514876365662},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.61018967628479},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.581167995929718},{"id":"https://openalex.org/keywords/spectral-bands","display_name":"Spectral bands","score":0.5633082985877991},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5563680529594421},{"id":"https://openalex.org/keywords/displacement","display_name":"Displacement (psychology)","score":0.5460444092750549},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5410287976264954},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.47424736618995667},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44906654953956604},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.44274473190307617},{"id":"https://openalex.org/keywords/full-spectral-imaging","display_name":"Full spectral imaging","score":0.414423406124115},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4131247401237488},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39722371101379395},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.28667372465133667},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19428327679634094},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.17175212502479553}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8928475975990295},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.7083485126495361},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6763514876365662},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.61018967628479},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.581167995929718},{"id":"https://openalex.org/C114700698","wikidata":"https://www.wikidata.org/wiki/Q2882278","display_name":"Spectral bands","level":2,"score":0.5633082985877991},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5563680529594421},{"id":"https://openalex.org/C107551265","wikidata":"https://www.wikidata.org/wiki/Q1458245","display_name":"Displacement (psychology)","level":2,"score":0.5460444092750549},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5410287976264954},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.47424736618995667},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44906654953956604},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.44274473190307617},{"id":"https://openalex.org/C78660771","wikidata":"https://www.wikidata.org/wiki/Q5508206","display_name":"Full spectral imaging","level":3,"score":0.414423406124115},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4131247401237488},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39722371101379395},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28667372465133667},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19428327679634094},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.17175212502479553},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3612016","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.6800000071525574,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G3354557426","display_name":null,"funder_award_id":"xhj032021017-04","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6491784331","display_name":null,"funder_award_id":"2020AAA0105600","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8695853166","display_name":null,"funder_award_id":"62006183","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"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W73112891","https://openalex.org/W764651262","https://openalex.org/W2120184245","https://openalex.org/W2127495569","https://openalex.org/W2133665775","https://openalex.org/W2153635508","https://openalex.org/W2163753106","https://openalex.org/W2478493250","https://openalex.org/W2520430674","https://openalex.org/W2560474170","https://openalex.org/W2806155925","https://openalex.org/W2909479956","https://openalex.org/W2942454403","https://openalex.org/W2962747489","https://openalex.org/W2963782415","https://openalex.org/W2991209609","https://openalex.org/W3013064625","https://openalex.org/W3098435832","https://openalex.org/W3103919952","https://openalex.org/W3118496544","https://openalex.org/W3154593456","https://openalex.org/W3159843306","https://openalex.org/W3215551818","https://openalex.org/W4200633942","https://openalex.org/W4210331506","https://openalex.org/W4214543554","https://openalex.org/W4226430688","https://openalex.org/W4293812270","https://openalex.org/W4312431312","https://openalex.org/W4316466768","https://openalex.org/W4385764496","https://openalex.org/W6600553734","https://openalex.org/W6733013215","https://openalex.org/W6842667185"],"related_works":["https://openalex.org/W2911259277","https://openalex.org/W2800956885","https://openalex.org/W4386427838","https://openalex.org/W2533019003","https://openalex.org/W2024377932","https://openalex.org/W1978077614","https://openalex.org/W2889956472","https://openalex.org/W2799746630","https://openalex.org/W2040117879","https://openalex.org/W1982418987"],"abstract_inverted_index":{"Spectral":[0,150],"modeling":[1],"plays":[2],"a":[3,29,97,124,171],"critical":[4],"role":[5],"in":[6,37,54],"denoising":[7,45],"hyperspectral":[8],"images":[9],"(HSIs),":[10],"with":[11],"recent":[12],"approaches":[13,27],"leveraging":[14],"well-designed":[15],"network":[16,46,85],"architectures":[17],"to":[18,60,86,92],"extract":[19],"spectral":[20,35,65,105,113,120,127,138],"contexts":[21,39],"for":[22,43,83,155,174],"noise":[23],"removal.":[24],"However,":[25],"these":[26],"overlook":[28],"striking":[30],"finding:":[31],"the":[32,44,48,55,61,71,84,112,117,133,142,149,189],"presence":[33],"of":[34,51,64,74,119,144,193],"differences":[36,106],"noisy":[38],"can":[40,79,131,168],"pose":[41],"challenges":[42],"during":[47],"restoration":[49],"process":[50],"each":[52],"band":[53],"HSI.":[56],"We":[57,95,146],"attribute":[58],"this":[59],"varying":[62],"levels":[63],"difference":[66],"between":[67,136,164],"different":[68,137],"bands":[69,139],"and":[70,140,157,161,191,196],"unknown":[72],"distribution":[73],"various":[75],"noises.":[76],"These":[77],"factors":[78],"make":[80],"it":[81],"difficult":[82],"capture":[87],"consistent":[88],"features,":[89],"ultimately":[90],"leading":[91],"suboptimal":[93],"solutions.":[94],"propose":[96],"novel":[98],"concept":[99,195],"termed":[100],"'spectral":[101],"displacement,'":[102],"which":[103],"views":[104],"as":[107,170],"pixel":[108],"motion":[109],"displacement":[110,159],"along":[111],"domain.":[114],"To":[115],"eliminate":[116],"effect":[118],"displacement,":[121],"we":[122],"introduce":[123],"potential":[125],"solution:":[126],"alignment.":[128],"This":[129],"approach":[130],"increase":[132],"mutual":[134],"information":[135],"enhance":[141],"effectiveness":[143,190],"denoising.":[145],"then":[147],"present":[148],"Alignment":[151],"Recurrent":[152],"Network":[153],"(SARN)":[154],"efficient":[156],"effective":[158],"estimation":[160],"pixel-level":[162],"alignment":[163],"neighboring":[165],"bands.":[166],"SARN":[167],"serve":[169],"general":[172],"plug-in":[173],"HSI":[175],"backbones":[176],"without":[177],"requiring":[178],"any":[179],"model-specific":[180],"design.":[181],"Experimental":[182],"results":[183],"on":[184],"several":[185],"benchmark":[186],"datasets":[187],"confirm":[188],"superiority":[192],"our":[194],"network.":[197],"The":[198],"source":[199],"code":[200],"will":[201],"be":[202],"available":[203],"at":[204],"https://github.com/MIV-XJTU/SARN.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
