{"id":"https://openalex.org/W3091380816","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206912","title":"Single Image Super-Resolution with Hierarchical Receptive Field","display_name":"Single Image Super-Resolution with Hierarchical Receptive Field","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3091380816","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206912","mag":"3091380816"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206912","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206912","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5003309570","display_name":"Din Qin","orcid":null},"institutions":[{"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":false,"raw_author_name":"Din Qin","raw_affiliation_strings":["Department of Electronic Engineering, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000141628","display_name":"Xiaodong Gu","orcid":"https://orcid.org/0000-0002-7096-1830"},"institutions":[{"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":false,"raw_author_name":"Xiaodong Gu","raw_affiliation_strings":["Department of Electronic Engineering, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"9906","issue":null,"first_page":"1","last_page":"8"},"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9926999807357788,"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":0.9897000193595886,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7901566028594971},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6177272796630859},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5848253965377808},{"id":"https://openalex.org/keywords/dilation","display_name":"Dilation (metric space)","score":0.5809140205383301},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.574997067451477},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5556610226631165},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5388332009315491},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.5029012560844421},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5028886198997498},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.47549164295196533},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.45866432785987854},{"id":"https://openalex.org/keywords/receptive-field","display_name":"Receptive field","score":0.447995126247406},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43607741594314575},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4204864203929901},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.41724318265914917},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39636772871017456},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2630797028541565},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1707799732685089}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7901566028594971},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6177272796630859},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5848253965377808},{"id":"https://openalex.org/C2780757906","wikidata":"https://www.wikidata.org/wiki/Q5276676","display_name":"Dilation (metric space)","level":2,"score":0.5809140205383301},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.574997067451477},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5556610226631165},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5388332009315491},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5029012560844421},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5028886198997498},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.47549164295196533},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.45866432785987854},{"id":"https://openalex.org/C19071747","wikidata":"https://www.wikidata.org/wiki/Q1755207","display_name":"Receptive field","level":2,"score":0.447995126247406},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43607741594314575},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4204864203929901},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.41724318265914917},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39636772871017456},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2630797028541565},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1707799732685089},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206912","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206912","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1791560514","https://openalex.org/W1885185971","https://openalex.org/W1930824406","https://openalex.org/W1992408872","https://openalex.org/W2047920195","https://openalex.org/W2097117768","https://openalex.org/W2110158442","https://openalex.org/W2110320844","https://openalex.org/W2133665775","https://openalex.org/W2157190232","https://openalex.org/W2183182206","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/W2607041014","https://openalex.org/W2739757502","https://openalex.org/W2747898905","https://openalex.org/W2780544323","https://openalex.org/W2795024892","https://openalex.org/W2907551576","https://openalex.org/W2963037581","https://openalex.org/W2963372104","https://openalex.org/W2963446712","https://openalex.org/W2963470893","https://openalex.org/W2963610452","https://openalex.org/W2963729050","https://openalex.org/W2963840672","https://openalex.org/W2964101377","https://openalex.org/W2964121744","https://openalex.org/W2964125708","https://openalex.org/W2964277374","https://openalex.org/W3101659800","https://openalex.org/W4377561911","https://openalex.org/W6631190155","https://openalex.org/W6696085341"],"related_works":["https://openalex.org/W2089544495","https://openalex.org/W2079003682","https://openalex.org/W1555021777","https://openalex.org/W2913266608","https://openalex.org/W2799648451","https://openalex.org/W1964918325","https://openalex.org/W2189496153","https://openalex.org/W2186491718","https://openalex.org/W2034008118","https://openalex.org/W2055164815"],"abstract_inverted_index":{"As":[0],"a":[1,79],"pixel-level":[2],"prediction":[3,153],"task,":[4],"it's":[5],"crucial":[6],"for":[7,196],"single":[8],"image":[9,30,155],"super-resolution":[10],"(SISR)":[11],"to":[12,27,41,62,104,135,141,167],"capture":[13],"contextual":[14,51],"information":[15,52],"over":[16],"the":[17,29,43,50,64,69,143,148,152,159,172],"multi-scale":[18,138],"regions":[19],"in":[20,32,113],"low-resolution":[21],"(LR)":[22],"space,":[23],"which":[24,74],"is":[25,75,162],"used":[26,186],"predict":[28,171],"details":[31,156],"high-resolution":[33],"(HR)":[34],"space.":[35],"So":[36],"researchers":[37],"proposed":[38],"multiple":[39],"methods":[40,195],"enhance":[42],"size":[44,70],"of":[45,53,59,66,71,82,154],"receptive":[46,100,106,116],"field":[47,101,107,117],"and":[48,77,85,97,128,146,165,170,177,199],"take":[49],"images":[54,176],"into":[55],"account.":[56],"But":[57],"most":[58],"them":[60],"tend":[61],"increase":[63],"depth":[65],"networks":[67],"or":[68],"kernels":[72],"simply,":[73],"inefficient":[76],"consumes":[78],"large":[80],"amount":[81],"computational":[83,110],"resources":[84],"memory.":[86],"In":[87],"this":[88],"paper,":[89],"we":[90,120],"combine":[91],"dilated":[92,129],"convolutions":[93,96,123,130],"with":[94,124,131],"standard":[95,122],"propose":[98],"hierarchical":[99,115],"network":[102],"(HRFN)":[103],"enlarge":[105],"without":[108],"additional":[109],"complexity.":[111],"Specially,":[112],"each":[114],"block":[118],"(HRFB),":[119],"apply":[121],"different":[125,132],"kernel":[126],"sizes":[127],"dilation":[133],"factors":[134],"adaptively":[136],"obtain":[137],"features.":[139],"Meanwhile,":[140],"ease":[142],"training":[144],"process":[145],"make":[147],"model":[149,191],"focus":[150],"on":[151,183],"(high-frequency":[157],"features),":[158],"residual":[160],"learning":[161],"adopted":[163],"locally":[164],"globally":[166],"explicitly":[168],"learn":[169],"difference":[173],"between":[174],"HR":[175],"LR":[178],"images.":[179],"Finally,":[180],"experimental":[181],"results":[182],"five":[184],"extensively":[185],"datasets":[187],"show":[188],"that":[189],"our":[190],"outperforms":[192],"those":[193],"state-of-the-art":[194],"both":[197],"quantitative":[198],"qualitative":[200],"comparisons.":[201]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
