{"id":"https://openalex.org/W4309852666","doi":"https://doi.org/10.3390/s22239110","title":"MLWAN: Multi-Scale Learning Wavelet Attention Module Network for Image Super Resolution","display_name":"MLWAN: Multi-Scale Learning Wavelet Attention Module Network for Image Super Resolution","publication_year":2022,"publication_date":"2022-11-24","ids":{"openalex":"https://openalex.org/W4309852666","doi":"https://doi.org/10.3390/s22239110","pmid":"https://pubmed.ncbi.nlm.nih.gov/36501811"},"language":"en","primary_location":{"id":"doi:10.3390/s22239110","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22239110","pdf_url":"https://www.mdpi.com/1424-8220/22/23/9110/pdf?version=1669272751","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/22/23/9110/pdf?version=1669272751","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100460567","display_name":"Jian Ma","orcid":"https://orcid.org/0000-0003-4448-592X"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]},{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jian Ma","raw_affiliation_strings":["School of Computer Science, Fudan University, Shanghai 200433, China","School of Internet, Anhui University, Hefei 230039, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai 200433, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Internet, Anhui University, Hefei 230039, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068628993","display_name":"Xiyu Han","orcid":"https://orcid.org/0000-0002-2592-5355"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiyu Han","raw_affiliation_strings":["School of Internet, Anhui University, Hefei 230039, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Internet, Anhui University, Hefei 230039, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084736096","display_name":"Xiaoyin Zhang","orcid":"https://orcid.org/0000-0001-6133-6796"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyin Zhang","raw_affiliation_strings":["School of Internet, Anhui University, Hefei 230039, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Internet, Anhui University, Hefei 230039, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100344732","display_name":"\u0416\u0438\u043f\u0435\u043d\u0433 \u041b\u0438","orcid":"https://orcid.org/0000-0002-0415-087X"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhipeng Li","raw_affiliation_strings":["School of Internet, Anhui University, Hefei 230039, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Internet, Anhui University, Hefei 230039, China","institution_ids":["https://openalex.org/I143868143"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100460567"],"corresponding_institution_ids":["https://openalex.org/I143868143","https://openalex.org/I24943067"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2673},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2673},"fwci":0.0812,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.34804939,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"22","issue":"23","first_page":"9110","last_page":"9110"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","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/T11105","display_name":"Advanced Image Processing Techniques","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":0.9991000294685364,"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":0.9980000257492065,"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/wavelet","display_name":"Wavelet","score":0.7442100048065186},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.741583526134491},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7267207503318787},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7231597900390625},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6328577995300293},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5390538573265076},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5179787874221802},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5102741718292236},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4829358458518982},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4607784152030945},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.45684799551963806},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4551742374897003},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4443521499633789},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40717270970344543},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14197099208831787}],"concepts":[{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.7442100048065186},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.741583526134491},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7267207503318787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7231597900390625},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6328577995300293},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5390538573265076},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5179787874221802},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5102741718292236},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4829358458518982},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4607784152030945},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.45684799551963806},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4551742374897003},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4443521499633789},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40717270970344543},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14197099208831787},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007858","descriptor_name":"Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007858","descriptor_name":"Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007858","descriptor_name":"Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D011996","descriptor_name":"Records","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011996","descriptor_name":"Records","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011996","descriptor_name":"Records","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014743","descriptor_name":"Videotape Recording","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014743","descriptor_name":"Videotape Recording","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014743","descriptor_name":"Videotape Recording","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":5,"locations":[{"id":"doi:10.3390/s22239110","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22239110","pdf_url":"https://www.mdpi.com/1424-8220/22/23/9110/pdf?version=1669272751","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:36501811","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36501811","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":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:606059288a9f47cd8a2577dc10471067","is_oa":true,"landing_page_url":"https://doaj.org/article/606059288a9f47cd8a2577dc10471067","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 22, Iss 23, p 9110 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/23/9110/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22239110","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"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":"Sensors; Volume 22; Issue 23; Pages: 9110","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:9741030","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9741030","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s22239110","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22239110","pdf_url":"https://www.mdpi.com/1424-8220/22/23/9110/pdf?version=1669272751","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2072979298","display_name":null,"funder_award_id":"61906118","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3607657900","display_name":null,"funder_award_id":"61906118","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G3909470083","display_name":null,"funder_award_id":"2108085MF230","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G4484303962","display_name":null,"funder_award_id":"2022M710745","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G4491462769","display_name":null,"funder_award_id":"2108085MF230","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7291288934","display_name":null,"funder_award_id":"2022M710745","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/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4309852666.pdf"},"referenced_works_count":67,"referenced_works":["https://openalex.org/W54257720","https://openalex.org/W1677182931","https://openalex.org/W1791560514","https://openalex.org/W1885185971","https://openalex.org/W1930824406","https://openalex.org/W1950594372","https://openalex.org/W1983364832","https://openalex.org/W1985806826","https://openalex.org/W2016482162","https://openalex.org/W2020455334","https://openalex.org/W2029684123","https://openalex.org/W2035677848","https://openalex.org/W2047920195","https://openalex.org/W2079302740","https://openalex.org/W2097074225","https://openalex.org/W2107589634","https://openalex.org/W2121058967","https://openalex.org/W2121927366","https://openalex.org/W2133665775","https://openalex.org/W2146200771","https://openalex.org/W2157494358","https://openalex.org/W2160547390","https://openalex.org/W2163935418","https://openalex.org/W2192954843","https://openalex.org/W2194775991","https://openalex.org/W2214802144","https://openalex.org/W2242218935","https://openalex.org/W2476548250","https://openalex.org/W2503339013","https://openalex.org/W2551161082","https://openalex.org/W2607041014","https://openalex.org/W2615439916","https://openalex.org/W2741137940","https://openalex.org/W2741196023","https://openalex.org/W2747898905","https://openalex.org/W2752782242","https://openalex.org/W2776107444","https://openalex.org/W2866634454","https://openalex.org/W2884585870","https://openalex.org/W2903251150","https://openalex.org/W2908513290","https://openalex.org/W2914022625","https://openalex.org/W2954930822","https://openalex.org/W2963037581","https://openalex.org/W2963372104","https://openalex.org/W2963470893","https://openalex.org/W2963494934","https://openalex.org/W2963495494","https://openalex.org/W2963583792","https://openalex.org/W2963610452","https://openalex.org/W2963645458","https://openalex.org/W2964101377","https://openalex.org/W2964125708","https://openalex.org/W2970971581","https://openalex.org/W2994323562","https://openalex.org/W3008359818","https://openalex.org/W3034552520","https://openalex.org/W3099091830","https://openalex.org/W3101659800","https://openalex.org/W3111132958","https://openalex.org/W4224091773","https://openalex.org/W4224229372","https://openalex.org/W4225658295","https://openalex.org/W4226449262","https://openalex.org/W6631190155","https://openalex.org/W6655801444","https://openalex.org/W6677548907"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2382174632","https://openalex.org/W2129959498","https://openalex.org/W2784060934","https://openalex.org/W2902714807","https://openalex.org/W2537489131","https://openalex.org/W2394084632","https://openalex.org/W2358293514","https://openalex.org/W2046633342","https://openalex.org/W2077021924"],"abstract_inverted_index":{"Image":[0],"super":[1],"resolution":[2,16],"(SR)":[3],"is":[4,138,145,188,201],"an":[5,181],"important":[6],"image":[7,37,66,103,124],"processing":[8],"technique":[9],"in":[10,33,51,59,220],"computer":[11],"vision":[12],"to":[13,70,147,166,177,190,203],"improve":[14],"the":[15,34,40,52,84,106,114,122,141,149,155,168,172,195,214,217],"of":[17,36,54,65,110,171,216,222],"images":[18],"and":[19,74,129,154,225],"videos.":[20],"In":[21,57,113,140,175],"recent":[22],"years,":[23],"deep":[24],"convolutional":[25,127],"neural":[26,160],"network":[27,99,192],"(CNN)":[28],"has":[29],"made":[30],"significant":[31],"progress":[32],"field":[35],"SR;":[38],"however,":[39],"existing":[41],"CNN-based":[42],"SR":[43,67],"methods":[44],"cannot":[45],"fully":[46],"search":[47],"for":[48,102],"background":[49],"information":[50],"measurement":[53],"feature":[55],"extraction.":[56],"addition,":[58],"most":[60],"cases,":[61],"different":[62,72,78,164],"scale":[63],"factors":[64],"are":[68,119],"assumed":[69],"be":[71],"assignments":[73],"completed":[75],"by":[76],"training":[77],"models,":[79],"which":[80],"does":[81],"not":[82],"meet":[83],"actual":[85],"application":[86],"requirements.":[87],"To":[88],"solve":[89],"these":[90],"problems,":[91],"we":[92],"propose":[93],"a":[94,131],"multi-scale":[95],"learning":[96],"wavelet":[97,152,169,198],"attention":[98,134,184],"(MLWAN)":[100],"model":[101,108,219],"SR.":[104],"Specifically,":[105],"proposed":[107,189,218],"consists":[109],"three":[111],"parts.":[112],"first":[115],"part,":[116,143],"low-level":[117],"features":[118],"extracted":[120],"from":[121],"input":[123],"through":[125],"two":[126],"layers,":[128],"then":[130],"new":[132],"channel-spatial":[133],"mechanism":[135],"(CSAM)":[136],"block":[137],"concatenated.":[139],"second":[142],"CNN":[144],"used":[146,202],"predict":[148,167],"highest-level":[150],"low-frequency":[151],"coefficients,":[153],"third":[156],"part":[157],"uses":[158],"recursive":[159],"networks":[161],"(RNN)":[162],"with":[163],"scales":[165],"coefficients":[170],"remaining":[173],"subbands.":[174],"order":[176],"further":[178],"achieve":[179],"lightweight,":[180],"effective":[182],"channel":[183],"recurrent":[185],"module":[186],"(ECARM)":[187],"reduce":[191],"parameters.":[193],"Finally,":[194],"inverse":[196],"discrete":[197],"transform":[199],"(IDWT)":[200],"reconstruct":[204],"HR":[205],"image.":[206],"Experimental":[207],"results":[208],"on":[209],"public":[210],"large-scale":[211],"datasets":[212],"demonstrate":[213],"superiority":[215],"terms":[221],"quantitative":[223],"indicators":[224],"visual":[226],"effects.":[227]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}
