{"id":"https://openalex.org/W3013405637","doi":"https://doi.org/10.1145/3373509.3373564","title":"A Multi-scale Wavelet CNN for Scanning Electron Microscopy Nerve Image Super Resolution","display_name":"A Multi-scale Wavelet CNN for Scanning Electron Microscopy Nerve Image Super Resolution","publication_year":2019,"publication_date":"2019-10-23","ids":{"openalex":"https://openalex.org/W3013405637","doi":"https://doi.org/10.1145/3373509.3373564","mag":"3013405637"},"language":"en","primary_location":{"id":"doi:10.1145/3373509.3373564","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3373509.3373564","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition","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":"https://openalex.org/A5059241804","display_name":"Pan Zhao","orcid":"https://orcid.org/0000-0001-9349-1125"},"institutions":[{"id":"https://openalex.org/I100188998","display_name":"Harbin University of Science and Technology","ror":"https://ror.org/04e6y1282","country_code":"CN","type":"education","lineage":["https://openalex.org/I100188998"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Pan Zhao","raw_affiliation_strings":["Harbin University of Science and Technology, Harbin"],"affiliations":[{"raw_affiliation_string":"Harbin University of Science and Technology, Harbin","institution_ids":["https://openalex.org/I100188998"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016581699","display_name":"Zhongwen Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I100188998","display_name":"Harbin University of Science and Technology","ror":"https://ror.org/04e6y1282","country_code":"CN","type":"education","lineage":["https://openalex.org/I100188998"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongwen Gao","raw_affiliation_strings":["Harbin University of Science and Technology, Harbin"],"affiliations":[{"raw_affiliation_string":"Harbin University of Science and Technology, Harbin","institution_ids":["https://openalex.org/I100188998"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100676754","display_name":"Hua Han","orcid":"https://orcid.org/0000-0003-4713-4631"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hua Han","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, Beijing"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100368565","display_name":"Guoqing Li","orcid":"https://orcid.org/0000-0002-2836-6413"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoqing Li","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, Beijing"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing","institution_ids":["https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5059241804"],"corresponding_institution_ids":["https://openalex.org/I100188998"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19407396,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"103","issue":null,"first_page":"213","last_page":"220"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9990000128746033,"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":0.9990000128746033,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9987999796867371,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.8571265339851379},{"id":"https://openalex.org/keywords/deconvolution","display_name":"Deconvolution","score":0.5668520927429199},{"id":"https://openalex.org/keywords/wavelet-packet-decomposition","display_name":"Wavelet packet decomposition","score":0.5039655566215515},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4879314601421356},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.48777276277542114},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.47204598784446716},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4709744453430176},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4351796507835388},{"id":"https://openalex.org/keywords/stationary-wavelet-transform","display_name":"Stationary wavelet transform","score":0.4284975230693817},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3891068994998932},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3856242895126343},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34243687987327576},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.29034101963043213},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.06273522973060608}],"concepts":[{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.8571265339851379},{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.5668520927429199},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.5039655566215515},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4879314601421356},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.48777276277542114},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.47204598784446716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4709744453430176},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4351796507835388},{"id":"https://openalex.org/C73339587","wikidata":"https://www.wikidata.org/wiki/Q1375942","display_name":"Stationary wavelet transform","level":5,"score":0.4284975230693817},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3891068994998932},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3856242895126343},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34243687987327576},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29034101963043213},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.06273522973060608},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3373509.3373564","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3373509.3373564","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1885185971","https://openalex.org/W1977937836","https://openalex.org/W2088254198","https://openalex.org/W2109773745","https://openalex.org/W2121058967","https://openalex.org/W2122632184","https://openalex.org/W2133665775","https://openalex.org/W2150385490","https://openalex.org/W2160547390","https://openalex.org/W2194775991","https://openalex.org/W2200594420","https://openalex.org/W2242218935","https://openalex.org/W2328510092","https://openalex.org/W2507235960","https://openalex.org/W2520930090","https://openalex.org/W2595967805","https://openalex.org/W2748225867","https://openalex.org/W2765713907","https://openalex.org/W2770464426","https://openalex.org/W2797634838","https://openalex.org/W2807593488","https://openalex.org/W3101707638","https://openalex.org/W3176874133","https://openalex.org/W6725327932"],"related_works":["https://openalex.org/W2085792030","https://openalex.org/W1588899229","https://openalex.org/W1967182499","https://openalex.org/W4321517526","https://openalex.org/W1976022598","https://openalex.org/W2391053410","https://openalex.org/W2111896212","https://openalex.org/W2001281573","https://openalex.org/W2097034666","https://openalex.org/W2360367699"],"abstract_inverted_index":{"Efficient":[0],"acquisition":[1],"of":[2,11,16,19,44,97,115,123,150,156,210,215,230,254],"high-resolution":[3],"SEM":[4,20,45,151],"nerve":[5,21,46,152,255],"images":[6,25,47],"is":[7,58,91,100,103,118,132,140,205,218,245],"an":[8,81],"important":[9],"part":[10],"brain":[12],"science":[13],"research.":[14],"Because":[15],"the":[17,23,40,54,67,74,94,98,112,116,121,128,147,172,176,182,187,195,197,208,213,216,223,231,252],"characteristics":[18,149],"image,":[22],"SR":[24,130,200],"obtained":[26,133],"by":[27,134],"current":[28],"methods":[29],"are":[30,70,168,233],"too":[31],"smooth":[32],"and":[33,48,66,109,127,163,175,202,222,227,248],"lack":[34],"detailed":[35],"information.":[36],"We":[37],"first":[38],"analyze":[39],"multi-scale":[41,89,124,164],"wavelet":[42,90,125,148,165,203,211,225],"coefficients":[43,166],"find":[49],"that":[50],"when":[51],"decomposed":[52],"to":[53,120,142,170,189],"third":[55],"scale,":[56,212],"there":[57],"almost":[59],"no":[60],"structural":[61],"information":[62],"at":[63],"high":[64],"frequencies":[65],"horizontal":[68],"components":[69],"more":[71,246,249],"obvious":[72],"than":[73],"vertical":[75],"ones.":[76],"Based":[77],"on":[78,88,146],"these":[79],"characteristics,":[80],"end-to-end":[82],"full":[83],"convolution":[84],"neural":[85],"network":[86,99,117,192],"based":[87,145],"proposed.":[92],"Firstly,":[93],"main":[95],"structure":[96,256],"constructed,":[101],"which":[102],"divided":[104],"into":[105],"two":[106],"modules:":[107],"encoding":[108],"decoding.":[110],"Then,":[111],"actual":[113],"output":[114],"changed":[119],"prediction":[122],"coefficients,":[126],"final":[129],"image":[131],"inverse":[135,183],"transformation.":[136],"Finally,":[137],"multi-objective":[138],"form":[139],"used":[141,169],"set":[143],"hyper-parameters":[144],"image.":[153],"In":[154,180,194],"terms":[155],"loss":[157,162],"function,":[158],"two-stage":[159],"losses":[160],"(gray-level":[161],"loss)":[167],"emphasize":[171],"overall":[173],"texture":[174],"high-frequency":[177],"details":[178],"respectively.":[179],"addition,":[181],"transformation":[184],"can":[185],"replace":[186],"deconvolution":[188],"reduce":[190],"some":[191],"parameters.":[193],"experiment,":[196],"relationship":[198],"between":[199],"effect":[201],"scale":[204,226],"analyzed.":[206],"With":[207],"increase":[209],"performance":[214],"model":[217,232],"not":[219],"linearly":[220],"enhanced,":[221],"optimal":[224],"upscaling":[228],"factor":[229],"determined.":[234],"Experiments":[235],"show":[236],"that,":[237],"compared":[238],"with":[239],"other":[240],"state-of-the-art":[241],"methods,":[242],"our":[243],"method":[244],"prominent":[247],"realistic":[250],"in":[251],"restoration":[253],"texture.":[257]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
