{"id":"https://openalex.org/W4394868094","doi":"https://doi.org/10.48550/arxiv.2404.09697","title":"HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising","display_name":"HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising","publication_year":2024,"publication_date":"2024-04-15","ids":{"openalex":"https://openalex.org/W4394868094","doi":"https://doi.org/10.48550/arxiv.2404.09697"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2404.09697","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2404.09697","pdf_url":"https://arxiv.org/pdf/2404.09697","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2404.09697","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100760789","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0003-1831-078X","observed_orcids":["https://orcid.org/0000-0003-1831-078X","https://orcid.org/0000-0001-7542-3110"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011475525","display_name":"Jiahua Xiao","orcid":"https://orcid.org/0000-0002-0469-528X","observed_orcids":["https://orcid.org/0000-0002-0469-528X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Jiahua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Song, Xiang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037365971","display_name":"Yu Guo","orcid":"https://orcid.org/0000-0002-0642-7684","observed_orcids":["https://orcid.org/0000-0002-0642-7684","https://orcid.org/0000-0003-2571-0247"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108651660","display_name":"Peilin Jiang","orcid":"https://orcid.org/0000-0003-0316-6631","observed_orcids":["https://orcid.org/0000-0003-0316-6631"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Peilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078879136","display_name":"Haiwei Yang","orcid":"https://orcid.org/0000-0003-2527-8335","observed_orcids":["https://orcid.org/0000-0003-2527-8335"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Haiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100455958","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0003-3462-8472","observed_orcids":["https://orcid.org/0000-0003-3462-8472","https://orcid.org/0000-0001-8296-8039"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Fei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"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":0.9961000084877014,"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":0.9961000084877014,"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":0.002199999988079071,"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/T11019","display_name":"Image Enhancement Techniques","score":0.0010999999940395355,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-image-denoising","display_name":"hyperspectral image denoising","score":1.0},{"id":"https://openalex.org/keywords/selective-state-space-models","display_name":"selective state space models","score":0.7329999804496765},{"id":"https://openalex.org/keywords/mamba","display_name":"Mamba","score":0.9340000152587891},{"id":"https://openalex.org/keywords/global-context-modeling","display_name":"global context modeling","score":0.7179999947547913},{"id":"https://openalex.org/keywords/spectral-attention","display_name":"spectral attention","score":0.7509999871253967},{"id":"https://openalex.org/keywords/long-term-dependencies","display_name":"long-range dependencies","score":0.5239999890327454},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.5440000295639038}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7551289796829224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6851789355278015},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5702193975448608},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5635187029838562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41718894243240356},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3319634795188904},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2404.09697","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2404.09697","pdf_url":"https://arxiv.org/pdf/2404.09697","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2404.09697","doi":"https://doi.org/10.48550/arxiv.2404.09697","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2404.09697","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2404.09697","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2404.09697","pdf_url":"https://arxiv.org/pdf/2404.09697","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4394868094.pdf","grobid_xml":"https://content.openalex.org/works/W4394868094.grobid-xml"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W2964121744","https://openalex.org/W3209374680","https://openalex.org/W4313442864","https://openalex.org/W4318620776","https://openalex.org/W4391013663","https://openalex.org/W4392576551","https://openalex.org/W4393764310","https://openalex.org/W4402952320","https://openalex.org/W4403760842","https://openalex.org/W4403842425"],"related_works":["https://openalex.org/W4401725555","https://openalex.org/W4408970860","https://openalex.org/W4414463179","https://openalex.org/W4382457956","https://openalex.org/W4416727441","https://openalex.org/W4410719639","https://openalex.org/W4417196258","https://openalex.org/W4412623779","https://openalex.org/W4409263991","https://openalex.org/W4307772935"],"abstract_inverted_index":{"Effectively":[0],"modeling":[1],"global":[2],"context":[3,105],"information":[4,93,124],"in":[5],"hyperspectral":[6],"image":[7],"(HSI)":[8],"denoising":[9,49,112,141],"is":[10,55],"crucial,":[11],"but":[12],"prevailing":[13],"methods":[14],"using":[15],"convolution":[16],"or":[17,22],"transformers":[18],"still":[19],"face":[20],"localized":[21],"computational":[23,38],"efficiency":[24,155],"limitations.":[25],"Inspired":[26],"by":[27,131,161],"the":[28,59,98,104,120,127,144,154,157],"emerging":[29],"Selective":[30],"State":[31,99],"Space":[32,100],"Model":[33,101],"(Mamba)":[34],"with":[35],"nearly":[36],"linear":[37],"complexity":[39],"and":[40,66,88,91,110,125,152],"efficient":[41],"long-term":[42],"modeling,":[43],"we":[44],"present":[45],"a":[46],"novel":[47],"HSI":[48,69,140],"network":[50],"named":[51],"HSIDMamba":[52],"(HSDM).":[53],"HSDM":[54,73,109],"tailored":[56],"to":[57,81,118],"exploit":[58],"capture":[60],"of":[61,108,122,147,156],"potential":[62],"spatial-spectral":[63,83],"dependencies":[64],"effectively":[65],"efficiently":[67],"for":[68],"denoising.":[70],"In":[71,116],"particular,":[72],"comprises":[74],"multiple":[75],"Hyperspectral":[76],"Continuous":[77],"Scan":[78],"Blocks":[79],"(HCSB)":[80],"strengthen":[82],"interactions.":[84],"HCSB":[85],"links":[86],"forward":[87],"backward":[89],"scans":[90],"enhances":[92],"from":[94],"eight":[95],"directions":[96],"through":[97],"(SSM),":[102],"strengthening":[103],"representation":[106],"learning":[107],"improving":[111],"performance":[113,146,151],"more":[114],"effectively.":[115],"addition,":[117],"enhance":[119],"utilization":[121],"spectral":[123,134],"mitigate":[126],"degradation":[128],"problem":[129],"caused":[130],"long-range":[132],"scanning,":[133],"attention":[135],"mechanism.":[136],"Extensive":[137],"evaluations":[138],"against":[139],"benchmarks":[142],"validate":[143],"superior":[145],"HSDM,":[148],"achieving":[149],"state-of-the-art":[150],"surpassing":[153],"transformer":[158],"method":[159],"SERT":[160],"31%.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":8}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2024-04-17T00:00:00"}