{"id":"https://openalex.org/W4291910336","doi":"https://doi.org/10.1109/lsp.2022.3199145","title":"CSformer: Cross-Scale Features Fusion Based Transformer for Image Denoising","display_name":"CSformer: Cross-Scale Features Fusion Based Transformer for Image Denoising","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4291910336","doi":"https://doi.org/10.1109/lsp.2022.3199145"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2022.3199145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2022.3199145","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-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/A5090531606","display_name":"Haitao Yin","orcid":"https://orcid.org/0000-0003-2975-2188"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haitao Yin","raw_affiliation_strings":["College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-2975-2188","affiliations":[{"raw_affiliation_string":"College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100759476","display_name":"Siyuan Ma","orcid":"https://orcid.org/0000-0002-3684-5879"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyuan Ma","raw_affiliation_strings":["College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I41198531"],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":1.7992,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.86103195,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"29","issue":null,"first_page":"1809","last_page":"1813"},"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":0.9994000196456909,"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":0.9983000159263611,"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/transformer","display_name":"Transformer","score":0.6453248858451843},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5976186394691467},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5857813954353333},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5314561128616333},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.4819502532482147},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3880999684333801},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17090073227882385},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.10272997617721558}],"concepts":[{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6453248858451843},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5976186394691467},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5857813954353333},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5314561128616333},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4819502532482147},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3880999684333801},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17090073227882385},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.10272997617721558},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2022.3199145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2022.3199145","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G10643888","display_name":"\u57fa\u4e8e\u5f20\u91cf\u7a00\u758f\u6df1\u5ea6\u5b66\u4e60\u7684\u9ad8\u7ef4\u9065\u611f\u56fe\u50cf\u878d\u5408\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61971237","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2056370875","https://openalex.org/W2097073572","https://openalex.org/W2110158442","https://openalex.org/W2121927366","https://openalex.org/W2153663612","https://openalex.org/W2503339013","https://openalex.org/W2505029951","https://openalex.org/W2508457857","https://openalex.org/W2571662414","https://openalex.org/W2613155248","https://openalex.org/W2741137940","https://openalex.org/W2764207251","https://openalex.org/W2784344583","https://openalex.org/W2888395215","https://openalex.org/W2963372104","https://openalex.org/W2964101377","https://openalex.org/W2976669726","https://openalex.org/W3086273127","https://openalex.org/W3096609285","https://openalex.org/W3110068926","https://openalex.org/W3121052081","https://openalex.org/W3126781302","https://openalex.org/W3136416617","https://openalex.org/W3138516171","https://openalex.org/W3167568784","https://openalex.org/W3171125843","https://openalex.org/W3182000414","https://openalex.org/W3207918547","https://openalex.org/W4205432963","https://openalex.org/W4206473685","https://openalex.org/W4285216227","https://openalex.org/W4312812783","https://openalex.org/W4312977443","https://openalex.org/W6739901393","https://openalex.org/W6784333009","https://openalex.org/W6788135285"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Window":[0],"self-attention":[1],"based":[2,37,116],"Transformer":[3,36,63,104,110,127],"receives":[4],"the":[5,12,21,52,74,92,97,102,108,113,124,133],"advanced":[6],"results":[7],"in":[8,19,72,128],"image":[9,38],"denoising.":[10],"However,":[11],"current":[13],"methods":[14],"still":[15],"have":[16],"some":[17,138],"limitations":[18],"capturing":[20],"global":[22],"dependencies":[23],"and":[24,58,112],"local":[25,129],"responses.":[26],"To":[27],"tackle":[28],"these":[29],"problems,":[30],"this":[31],"paper":[32],"proposes":[33],"a":[34,68],"novel":[35],"denoising":[39],"method,":[40],"called":[41],"as":[42],"CSformer,":[43],"which":[44,73,121],"is":[45],"equipped":[46],"with":[47,145],"two":[48],"key":[49],"blocks,":[50],"including":[51],"cross-scale":[53,87],"features":[54],"fusion":[55,88],"(CS2F)":[56],"block":[57,105,111],"mixed":[59],"global-local":[60],"Swin":[61,109],"(M-Swin)":[62],"block.":[64,85],"The":[65],"CSformer":[66,136],"has":[67],"specific":[69],"multi-scale":[70,75,98],"framework,":[71],"features,":[76,93],"extracted":[77],"by":[78],"M-Swin":[79,103],"Transformer,":[80],"are":[81],"fused":[82],"using":[83],"CS2F":[84],"Such":[86],"not":[89],"only":[90],"enriches":[91],"but":[94],"also":[95],"yields":[96],"self-attention.":[99],"In":[100],"addition,":[101],"consists":[106],"of":[107,126,135],"separable":[114],"convolution":[115],"convolutional":[117],"local-extraction":[118],"(CLE)":[119],"block,":[120],"can":[122],"boost":[123],"ability":[125],"representation.":[130],"We":[131],"demonstrate":[132],"superiority":[134],"on":[137],"well-known":[139],"datasets":[140],"at":[141],"different":[142],"noise":[143],"levels":[144],"comparisons":[146],"to":[147],"several":[148],"state-of-the-art":[149],"methods.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":8}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
