{"id":"https://openalex.org/W7105692337","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228650","title":"EFSNet: Efficient Frequency Selective Network for Hyperspectral Image Super-Resolution","display_name":"EFSNet: Efficient Frequency Selective Network for Hyperspectral Image Super-Resolution","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W7105692337","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228650"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228650","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228650","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":null,"display_name":"Baisong Li","orcid":null},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baisong Li","raw_affiliation_strings":["Jilin University,College of Computer Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jilin University,College of Computer Science and Technology","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xingwang Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingwang Wang","raw_affiliation_strings":["Jilin University,College of Computer Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jilin University,College of Computer Science and Technology","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":null,"display_name":"Haixiao Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haixiao Xu","raw_affiliation_strings":["Jilin University,College of Computer Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jilin University,College of Computer Science and Technology","institution_ids":["https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I194450716"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.56096023,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.7037000060081482,"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":0.7037000060081482,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.23600000143051147,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.017000000923871994,"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-imaging","display_name":"Hyperspectral imaging","score":0.6735000014305115},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5999000072479248},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5920000076293945},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5558000206947327},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5303000211715698},{"id":"https://openalex.org/keywords/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.48410001397132874},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47859999537467957},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.4722000062465668},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.3912999927997589}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6735000014305115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6650000214576721},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5999000072479248},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5920000076293945},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5740000009536743},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5558000206947327},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5303000211715698},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.48410001397132874},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47859999537467957},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4722000062465668},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40299999713897705},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.3912999927997589},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.35899999737739563},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C111350171","wikidata":"https://www.wikidata.org/wiki/Q7443700","display_name":"Second-generation wavelet transform","level":5,"score":0.34310001134872437},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.34150001406669617},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3328999876976013},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C95722684","wikidata":"https://www.wikidata.org/wiki/Q2622756","display_name":"Continuous wavelet transform","level":5,"score":0.26989999413490295},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C73339587","wikidata":"https://www.wikidata.org/wiki/Q1375942","display_name":"Stationary wavelet transform","level":5,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228650","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228650","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2133665775","https://openalex.org/W2615706402","https://openalex.org/W2621121458","https://openalex.org/W2767522909","https://openalex.org/W2773850265","https://openalex.org/W2931993473","https://openalex.org/W2939193868","https://openalex.org/W2962907479","https://openalex.org/W2970530557","https://openalex.org/W3025440211","https://openalex.org/W3027120144","https://openalex.org/W3043181422","https://openalex.org/W3128204528","https://openalex.org/W3167568784","https://openalex.org/W3197023476","https://openalex.org/W3201952986","https://openalex.org/W3207516177","https://openalex.org/W3207918547","https://openalex.org/W3211879038","https://openalex.org/W4293363567","https://openalex.org/W4386047745","https://openalex.org/W4386766980","https://openalex.org/W4388624366","https://openalex.org/W4390873790","https://openalex.org/W4391341259","https://openalex.org/W4394596593","https://openalex.org/W4398175752","https://openalex.org/W4399513025","https://openalex.org/W4402264354","https://openalex.org/W4402952320","https://openalex.org/W4403071009","https://openalex.org/W4403889066","https://openalex.org/W4405304078","https://openalex.org/W4406611003","https://openalex.org/W4407098043","https://openalex.org/W4408352122","https://openalex.org/W4415796023"],"related_works":[],"abstract_inverted_index":{"Hyperspectral":[0],"image":[1,124],"super-resolution":[2],"(SR)":[3],"aims":[4],"to":[5,42,58,92,102],"reconstruct":[6],"high-resolution":[7],"images":[8],"from":[9],"low-resolution":[10],"hyperspectral":[11,123],"observations.":[12],"Given":[13],"the":[14,32,53,56,100,105],"significant":[15],"differences":[16],"in":[17,24,31,122],"feature":[18],"dependencies":[19],"across":[20],"various":[21],"frequency":[22,33,47,82,95,108],"domains":[23],"SR":[25],"tasks,":[26],"some":[27],"methods":[28,129],"perform":[29],"reconstruction":[30],"domain.":[34],"However,":[35],"directly":[36],"applying":[37],"Fourier":[38],"or":[39],"wavelet":[40,85,88],"transforms":[41],"decompose":[43],"features":[44,79],"into":[45,80],"fixed":[46],"components":[48,109],"lacks":[49],"adaptability,":[50],"which":[51,77],"limits":[52],"ability":[54],"of":[55],"network":[57,101],"dynamically":[59],"focus":[60],"on":[61,130],"high-value":[62],"features.":[63],"To":[64],"address":[65],"this":[66,68],"limitation,":[67],"paper":[69],"introduces":[70],"a":[71],"Dynamic":[72],"Spectral":[73],"Frequency":[74,115],"Selection":[75],"Model,":[76],"decomposes":[78],"disentangled":[81],"sub-bands.":[83],"Specifically,":[84],"transform":[86],"and":[87,97],"decomposition":[89],"are":[90],"combined":[91],"achieve":[93],"efficient":[94],"separation":[96],"selection,":[98],"allowing":[99],"adaptively":[103],"capture":[104],"most":[106],"informative":[107],"for":[110],"reconstruction.":[111],"The":[112],"proposed":[113],"Efficient":[114],"Selective":[116],"Network":[117],"(EFSNet)":[118],"achieves":[119],"state-of-the-art":[120],"performance":[121],"SR,":[125],"consistently":[126],"surpassing":[127],"existing":[128],"three":[131],"publicly":[132],"available":[133],"datasets.":[134]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-11-14T00:00:00"}
