{"id":"https://openalex.org/W4384159564","doi":"https://doi.org/10.1109/tnnls.2023.3289958","title":"Wavelet Pyramid Recurrent Structure-Preserving Attention Network for Single Image Super-Resolution","display_name":"Wavelet Pyramid Recurrent Structure-Preserving Attention Network for Single Image Super-Resolution","publication_year":2023,"publication_date":"2023-07-13","ids":{"openalex":"https://openalex.org/W4384159564","doi":"https://doi.org/10.1109/tnnls.2023.3289958","pmid":"https://pubmed.ncbi.nlm.nih.gov/37440374"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2023.3289958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2023.3289958","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5042420817","display_name":"Wei\u2010Yen Hsu","orcid":"https://orcid.org/0000-0002-4599-0744"},"institutions":[{"id":"https://openalex.org/I148099254","display_name":"National Chung Cheng University","ror":"https://ror.org/0028v3876","country_code":"TW","type":"education","lineage":["https://openalex.org/I148099254"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Wei-Yen Hsu","raw_affiliation_strings":["Department of Information Management, Advanced Institute of Manufacturing With High-Tech Innovations, and the Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, Chiayi, Taiwan","Department of Information Management, the Advanced Institute of Manufacturing With High-Tech Innovations, and the Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, Chiayi, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-4599-0744","affiliations":[{"raw_affiliation_string":"Department of Information Management, Advanced Institute of Manufacturing With High-Tech Innovations, and the Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, Chiayi, Taiwan","institution_ids":["https://openalex.org/I148099254"]},{"raw_affiliation_string":"Department of Information Management, the Advanced Institute of Manufacturing With High-Tech Innovations, and the Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, Chiayi, Taiwan","institution_ids":["https://openalex.org/I148099254"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040440046","display_name":"Pei-Wen Jian","orcid":"https://orcid.org/0009-0005-8858-2934"},"institutions":[{"id":"https://openalex.org/I148099254","display_name":"National Chung Cheng University","ror":"https://ror.org/0028v3876","country_code":"TW","type":"education","lineage":["https://openalex.org/I148099254"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Pei-Wen Jian","raw_affiliation_strings":["Department of Information Management, National Chung Cheng University, Chiayi, Taiwan"],"raw_orcid":"https://orcid.org/0009-0005-8858-2934","affiliations":[{"raw_affiliation_string":"Department of Information Management, National Chung Cheng University, Chiayi, Taiwan","institution_ids":["https://openalex.org/I148099254"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5042420817"],"corresponding_institution_ids":["https://openalex.org/I148099254"],"apc_list":null,"apc_paid":null,"fwci":3.7118,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.94758248,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"35","issue":"11","first_page":"15772","last_page":"15786"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":1.0,"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/T11105","display_name":"Advanced Image Processing Techniques","score":1.0,"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":0.9991999864578247,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9991000294685364,"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/pyramid","display_name":"Pyramid (geometry)","score":0.7982116937637329},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6652406454086304},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.6293313503265381},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6030048131942749},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5211108326911926},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5156099200248718},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.49957704544067383},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44763171672821045},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2165999710559845},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.04272374510765076}],"concepts":[{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.7982116937637329},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6652406454086304},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6293313503265381},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6030048131942749},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5211108326911926},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5156099200248718},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.49957704544067383},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44763171672821045},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2165999710559845},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.04272374510765076}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2023.3289958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2023.3289958","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:37440374","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37440374","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.550000011920929}],"awards":[{"id":"https://openalex.org/G2033305886","display_name":null,"funder_award_id":"MOST111-2410-H-194-038-MY3","funder_id":"https://openalex.org/F4320322795","funder_display_name":"Ministry of Science and Technology, Taiwan"},{"id":"https://openalex.org/G7199353477","display_name":null,"funder_award_id":"MOST110-2221-E-194-027-MY3","funder_id":"https://openalex.org/F4320322795","funder_display_name":"Ministry of Science and Technology, Taiwan"}],"funders":[{"id":"https://openalex.org/F4320322795","display_name":"Ministry of Science and Technology, Taiwan","ror":"https://ror.org/02kv4zf79"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W935139217","https://openalex.org/W1791560514","https://openalex.org/W1885185971","https://openalex.org/W1919542679","https://openalex.org/W1930824406","https://openalex.org/W2047920195","https://openalex.org/W2049237558","https://openalex.org/W2079302740","https://openalex.org/W2080392209","https://openalex.org/W2096916265","https://openalex.org/W2109773745","https://openalex.org/W2110158442","https://openalex.org/W2111454493","https://openalex.org/W2117539524","https://openalex.org/W2121281940","https://openalex.org/W2123613719","https://openalex.org/W2132710243","https://openalex.org/W2138088051","https://openalex.org/W2156825886","https://openalex.org/W2242218935","https://openalex.org/W2343734370","https://openalex.org/W2469023256","https://openalex.org/W2476548250","https://openalex.org/W2592088493","https://openalex.org/W2738280780","https://openalex.org/W2741196023","https://openalex.org/W2776107444","https://openalex.org/W2780544323","https://openalex.org/W2784332378","https://openalex.org/W2866634454","https://openalex.org/W2963182372","https://openalex.org/W2963372104","https://openalex.org/W2963470893","https://openalex.org/W2963494934","https://openalex.org/W2964101377","https://openalex.org/W2969200668","https://openalex.org/W3032400974","https://openalex.org/W3041293645","https://openalex.org/W3088103684","https://openalex.org/W3096993895","https://openalex.org/W3104196124","https://openalex.org/W3106754126","https://openalex.org/W3112693297","https://openalex.org/W3123866747","https://openalex.org/W3139100016","https://openalex.org/W3165892790","https://openalex.org/W3187032993","https://openalex.org/W4205276794","https://openalex.org/W4281702153","https://openalex.org/W4289523194","https://openalex.org/W4306167924","https://openalex.org/W4307092353","https://openalex.org/W4313476038","https://openalex.org/W4323913936","https://openalex.org/W4382657967","https://openalex.org/W6838985954","https://openalex.org/W6843667492","https://openalex.org/W6846179074"],"related_works":["https://openalex.org/W2382174632","https://openalex.org/W2129959498","https://openalex.org/W2784060934","https://openalex.org/W4249847449","https://openalex.org/W2902714807","https://openalex.org/W2537489131","https://openalex.org/W2046633342","https://openalex.org/W2394084632","https://openalex.org/W2358293514","https://openalex.org/W2056165575"],"abstract_inverted_index":{"Many":[0],"single":[1],"image":[2,59,66,88,159],"super-resolution":[3],"(SISR)":[4],"methods":[5],"that":[6,78,120,249],"use":[7],"convolutional":[8],"neural":[9],"networks":[10],"(CNNs)":[11],"learn":[12,132],"the":[13,23,32,45,58,61,81,162,185,205,229,250,260],"relationship":[14],"between":[15],"low-":[16,101,232],"and":[17,26,36,42,102,109,131,155,178,215,233,256,265,274],"high-resolution":[18],"images":[19,192],"directly,":[20],"without":[21],"considering":[22],"context":[24,272],"structure":[25,115,133,143,160,197,207,273],"detail":[27,179,188,213],"fidelity.":[28],"This":[29,125,200],"can":[30,152],"limit":[31],"potential":[33],"of":[34,164,187,231,271],"CNNs":[35],"result":[37],"in":[38,44,65,190,269],"unrealistic,":[39],"distorted":[40],"edges":[41],"textures":[43],"reconstructed":[46],"images.":[47],"A":[48],"more":[49],"effective":[50],"approach":[51],"is":[52,224],"to":[53,63,98,129,183,203,227],"incorporate":[54,130],"prior":[55],"knowledge":[56],"about":[57],"into":[60],"model":[62],"aid":[64],"reconstruction.":[67],"In":[68],"this":[69],"study,":[70],"we":[71,151,170],"propose":[72,112,172],"a":[73,113,220],"novel":[74,114,173],"recurrent":[75,93],"structure-preserving":[76,94],"mechanism":[77],"innovatively":[79],"uses":[80],"multiscale":[82],"wavelet":[83,91],"transform":[84],"(WT)":[85],"as":[86],"an":[87],"prior,":[89],"namely,":[90],"pyramid":[92],"attention":[95],"network":[96],"(WRSANet),":[97],"process":[99],"both":[100],"high-frequency":[103,191,210,234],"subnetworks":[104,135],"at":[105,136,166,236],"each":[106,137],"level":[107],"separately":[108],"recursively.":[110],"We":[111],"scale":[116,144],"preservation":[117,134,198],"(SSP)":[118],"architecture":[119,126],"differs":[121],"from":[122,196],"traditional":[123],"WTs.":[124],"allows":[127,201],"us":[128,202],"level.":[138],"By":[139],"using":[140],"our":[141],"proposed":[142,251],"fusion":[145,230],"(SSF)":[146],"combined":[147],"with":[148,239],"inverse":[149],"WT,":[150],"recursively":[153],"restore":[154],"preserve":[156,204],"rich":[157],"low-frequency":[158,206],"through":[161],"combination":[163],"SSP":[165],"various":[167],"levels.":[168],"Furthermore,":[169],"also":[171,225],"low-to-high-frequency":[174],"information":[175,195,235],"transmission":[176],"(L2HIT)":[177],"enhancement":[180],"(DE)":[181],"mechanisms":[182],"address":[184],"issue":[186],"distortion":[189],"by":[193],"transferring":[194],"subnetworks.":[199],"while":[208],"reconstructing":[209],"details,":[211],"improving":[212],"fidelity":[214],"avoiding":[216],"structural":[217],"distortion.":[218],"Finally,":[219],"joint":[221],"loss":[222],"function":[223],"used":[226],"balance":[228],"different":[237],"degrees,":[238],"hyperparameters":[240],"being":[241],"adjusted":[242],"during":[243],"training.":[244],"The":[245],"experimental":[246],"results":[247],"demonstrate":[248],"WRSANet":[252],"achieves":[253],"better":[254],"performance":[255],"visual":[257],"presentation":[258],"than":[259],"state-of-the-art":[261],"(SOTA)":[262],"on":[263],"synthetic":[264],"real":[266],"datasets,":[267],"especially":[268],"terms":[270],"texture":[275],"details.":[276]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":2}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}
