{"id":"https://openalex.org/W3133711492","doi":"https://doi.org/10.1109/dicta51227.2020.9363375","title":"Attentive Inception Module based Convolutional Neural Network for Image Enhancement","display_name":"Attentive Inception Module based Convolutional Neural Network for Image Enhancement","publication_year":2020,"publication_date":"2020-11-29","ids":{"openalex":"https://openalex.org/W3133711492","doi":"https://doi.org/10.1109/dicta51227.2020.9363375","mag":"3133711492"},"language":"en","primary_location":{"id":"doi:10.1109/dicta51227.2020.9363375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta51227.2020.9363375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","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/A5056349395","display_name":"Purbaditya Bhattacharya","orcid":"https://orcid.org/0000-0003-1679-0900"},"institutions":[{"id":"https://openalex.org/I190134885","display_name":"Helmut Schmidt University","ror":"https://ror.org/04e8jbs38","country_code":"DE","type":"education","lineage":["https://openalex.org/I190134885"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Purbaditya Bhattacharya","raw_affiliation_strings":["Helmut Schmidt University,Dept. of Signal Processing and Communication,Hamburg,Germany","Dept. of Signal Processing and Communication, Helmut Schmidt University, Hamburg, Germany"],"affiliations":[{"raw_affiliation_string":"Helmut Schmidt University,Dept. of Signal Processing and Communication,Hamburg,Germany","institution_ids":["https://openalex.org/I190134885"]},{"raw_affiliation_string":"Dept. of Signal Processing and Communication, Helmut Schmidt University, Hamburg, Germany","institution_ids":["https://openalex.org/I190134885"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110746452","display_name":"Udo Z\u00f6lzer","orcid":null},"institutions":[{"id":"https://openalex.org/I190134885","display_name":"Helmut Schmidt University","ror":"https://ror.org/04e8jbs38","country_code":"DE","type":"education","lineage":["https://openalex.org/I190134885"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Udo Zolzer","raw_affiliation_strings":["Helmut Schmidt University,Dept. of Signal Processing and Communication,Hamburg,Germany","Dept. of Signal Processing and Communication, Helmut Schmidt University, Hamburg, Germany"],"affiliations":[{"raw_affiliation_string":"Helmut Schmidt University,Dept. of Signal Processing and Communication,Hamburg,Germany","institution_ids":["https://openalex.org/I190134885"]},{"raw_affiliation_string":"Dept. of Signal Processing and Communication, Helmut Schmidt University, Hamburg, Germany","institution_ids":["https://openalex.org/I190134885"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5056349395"],"corresponding_institution_ids":["https://openalex.org/I190134885"],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.44192377,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"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":1.0,"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":1.0,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9998000264167786,"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/computer-science","display_name":"Computer science","score":0.7733895778656006},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.6678891181945801},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6602684259414673},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5729125142097473},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5017271041870117},{"id":"https://openalex.org/keywords/dilation","display_name":"Dilation (metric space)","score":0.49852514266967773},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4948119521141052},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.47701525688171387},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4744457006454468},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4580361843109131},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3844678997993469},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0777481198310852}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7733895778656006},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6678891181945801},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6602684259414673},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5729125142097473},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5017271041870117},{"id":"https://openalex.org/C2780757906","wikidata":"https://www.wikidata.org/wiki/Q5276676","display_name":"Dilation (metric space)","level":2,"score":0.49852514266967773},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4948119521141052},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.47701525688171387},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4744457006454468},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4580361843109131},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3844678997993469},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0777481198310852},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dicta51227.2020.9363375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta51227.2020.9363375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Digital Image Computing: Techniques and Applications (DICTA)","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":49,"referenced_works":["https://openalex.org/W1514535095","https://openalex.org/W1522301498","https://openalex.org/W1677182931","https://openalex.org/W1791560514","https://openalex.org/W1836465849","https://openalex.org/W1885185971","https://openalex.org/W1902237438","https://openalex.org/W1963882359","https://openalex.org/W2047920195","https://openalex.org/W2071804214","https://openalex.org/W2121927366","https://openalex.org/W2133564696","https://openalex.org/W2135065661","https://openalex.org/W2142683286","https://openalex.org/W2214802144","https://openalex.org/W2242218935","https://openalex.org/W2476548250","https://openalex.org/W2482139312","https://openalex.org/W2508457857","https://openalex.org/W2520164769","https://openalex.org/W2557216475","https://openalex.org/W2607041014","https://openalex.org/W2739757502","https://openalex.org/W2747898905","https://openalex.org/W2752782242","https://openalex.org/W2791799751","https://openalex.org/W2866634454","https://openalex.org/W2949117887","https://openalex.org/W2953066166","https://openalex.org/W2953133772","https://openalex.org/W2963372104","https://openalex.org/W2963420686","https://openalex.org/W2963494934","https://openalex.org/W2964046669","https://openalex.org/W2964101377","https://openalex.org/W2964121744","https://openalex.org/W2964125708","https://openalex.org/W2964308564","https://openalex.org/W2977534631","https://openalex.org/W2978824489","https://openalex.org/W3105540235","https://openalex.org/W3182211633","https://openalex.org/W6630875275","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6679434410","https://openalex.org/W6716109767","https://openalex.org/W6726381175","https://openalex.org/W6764750447"],"related_works":["https://openalex.org/W2180954594","https://openalex.org/W2052835778","https://openalex.org/W2049003611","https://openalex.org/W2127804977","https://openalex.org/W2108418243","https://openalex.org/W164103134","https://openalex.org/W2964954556","https://openalex.org/W2051113792","https://openalex.org/W4287548622","https://openalex.org/W3173347409"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"the":[3,9,39,91,96,101,110,141,145,149],"problem":[4],"of":[5,11,44,95,100,116],"image":[6,13],"enhancement":[7],"in":[8,38,73,87,129],"form":[10],"single":[12],"superresolution":[14],"and":[15,127,148],"compression":[16],"artifact":[17],"reduction":[18],"is":[19,84,138],"addressed":[20],"by":[21],"proposing":[22],"a":[23,79,106,152],"convolutional":[24],"neural":[25],"network":[26,40,125,137],"with":[27,49,123],"an":[28,32,64],"inception":[29,36],"module":[30,37],"containing":[31],"attention":[33,65,103],"mechanism.":[34],"The":[35,56,135],"contains":[41],"parallel":[42],"branches":[43],"convolution":[45],"layers":[46],"employing":[47],"filters":[48],"multiple":[50,124],"receptive":[51],"fields":[52],"via":[53,63],"filter":[54],"dilation.":[55],"aggregated":[57],"multi-scale":[58],"features":[59],"are":[60,121],"subsequently":[61],"filtered":[62],"mechanism":[66],"which":[67,112],"allows":[68],"learned":[69],"feature":[70,93],"map":[71],"weighting":[72],"order":[74,88,130],"to":[75,89,109,131],"reduce":[76],"redundancy.":[77],"Additionally,":[78],"long":[80],"skip":[81],"attentive":[82],"connection":[83],"also":[85],"introduced":[86],"process":[90],"penultimate":[92],"layer":[94],"proposed":[97],"network.":[98],"Addition":[99],"aforementioned":[102,146],"modules":[104],"introduce":[105],"dynamic":[107],"nature":[108],"model":[111],"would":[113],"otherwise":[114],"consist":[115],"static":[117],"trained":[118],"filters.":[119],"Experiments":[120],"performed":[122],"depths":[126],"architectures":[128],"assess":[132],"their":[133],"contributions.":[134],"final":[136],"evaluated":[139],"on":[140],"benchmark":[142],"datasets":[143],"for":[144],"tasks,":[147],"results":[150],"indicate":[151],"very":[153],"good":[154],"performance.":[155]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}