{"id":"https://openalex.org/W3034818018","doi":"https://doi.org/10.1109/icme46284.2020.9102784","title":"MGHCNET: A Deep Multi-Scale Granular and Holistic Channel Feature Generation Network for Image Super Resolution","display_name":"MGHCNET: A Deep Multi-Scale Granular and Holistic Channel Feature Generation Network for Image Super Resolution","publication_year":2020,"publication_date":"2020-06-09","ids":{"openalex":"https://openalex.org/W3034818018","doi":"https://doi.org/10.1109/icme46284.2020.9102784","mag":"3034818018"},"language":"en","primary_location":{"id":"doi:10.1109/icme46284.2020.9102784","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme46284.2020.9102784","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multimedia and Expo (ICME)","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/A5074650143","display_name":"Alireza Esmaeilzehi","orcid":"https://orcid.org/0000-0002-3625-1608"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Alireza Esmaeilzehi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","Concordia University Department of Electrical and Computer Engineering Montr\u00e9al QC Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Concordia University Department of Electrical and Computer Engineering Montr\u00e9al QC Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068820891","display_name":"M. Omair Ahmad","orcid":"https://orcid.org/0000-0002-2924-6659"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"M. Omair Ahmad","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","Concordia University Department of Electrical and Computer Engineering Montr\u00e9al QC Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Concordia University Department of Electrical and Computer Engineering Montr\u00e9al QC Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013967994","display_name":"M.N.S. Swamy","orcid":"https://orcid.org/0000-0002-3989-5476"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"M.N.S. Swamy","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","Concordia University Department of Electrical and Computer Engineering Montr\u00e9al QC Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Concordia University Department of Electrical and Computer Engineering Montr\u00e9al QC Canada","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9973999857902527,"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":0.9939000010490417,"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/residual","display_name":"Residual","score":0.76473069190979},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7568408250808716},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7120908498764038},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6967312097549438},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6433612108230591},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5708540678024292},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5404736399650574},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5123456716537476},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4798157215118408},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46467500925064087},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.440116286277771},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.17518845200538635},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11321285367012024},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.06427347660064697}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.76473069190979},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7568408250808716},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7120908498764038},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6967312097549438},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6433612108230591},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5708540678024292},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5404736399650574},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5123456716537476},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4798157215118408},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46467500925064087},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.440116286277771},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.17518845200538635},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11321285367012024},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.06427347660064697},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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},{"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme46284.2020.9102784","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme46284.2020.9102784","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1791560514","https://openalex.org/W1885185971","https://openalex.org/W1930824406","https://openalex.org/W2047920195","https://openalex.org/W2121927366","https://openalex.org/W2214802144","https://openalex.org/W2242218935","https://openalex.org/W2607041014","https://openalex.org/W2741137940","https://openalex.org/W2747898905","https://openalex.org/W2795024892","https://openalex.org/W2866634454","https://openalex.org/W2897811496","https://openalex.org/W2928165649","https://openalex.org/W2943486172","https://openalex.org/W2963372104","https://openalex.org/W2963610452","https://openalex.org/W2963645458","https://openalex.org/W2964125708","https://openalex.org/W6749061506","https://openalex.org/W6753074096","https://openalex.org/W6762406767"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2028665553","https://openalex.org/W4230315250","https://openalex.org/W2086519370","https://openalex.org/W2972212393"],"abstract_inverted_index":{"Residual":[0],"blocks":[1],"use":[2],"skip":[3],"connections":[4],"in":[5,13,26,40],"order":[6,77],"to":[7,78],"facilitate":[8],"the":[9,14,51,70,80,84,91,123,126,131],"flow":[10],"of":[11,72,83],"information":[12],"network":[15,21,52,85,124],"and":[16,107,113],"thus,":[17],"provide":[18],"a":[19,27,35,41,57],"good":[20],"performance.":[22,53],"As":[23],"different":[24,32,97],"objects":[25],"generic":[28],"image":[29,45,73],"appear":[30],"at":[31,64],"scales,":[33],"employing":[34],"multi-scale":[36,102],"feature":[37,98,105,111,117],"generation":[38,99,106],"module":[39],"residual":[42,59,93,128],"block":[43,60,94,129],"for":[44,69],"super":[46,74,134],"resolution":[47,135],"can":[48],"further":[49],"improve":[50],"In":[54,76],"this":[55],"paper,":[56],"new":[58],"that":[61,122],"generates":[62],"features":[63],"multiple":[65],"scales":[66],"is":[67,120],"proposed":[68,92,127],"task":[71],"resolution.":[75],"enhance":[79],"representational":[81],"capability":[82],"while":[86],"keeping":[87],"its":[88],"complexity":[89],"low,":[90],"uses":[95],"two":[96],"techniques,":[100],"namely,":[101],"granular":[103],"channel":[104,110],"uni-scale":[108],"holistic":[109],"generation,":[112],"fuses":[114],"their":[115],"output":[116],"maps.":[118],"It":[119],"shown":[121],"using":[125],"outperforms":[130],"state-of-the-art":[132],"lightweight":[133],"networks":[136],"on":[137],"four":[138],"benchmark":[139],"datasets":[140],"with":[141],"various":[142],"scaling":[143],"factors.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
