{"id":"https://openalex.org/W4390303882","doi":"https://doi.org/10.34768/amcs-2022-0009","title":"Performance analysis of a dual stage deep rain streak removal convolution neural network module with a modified deep residual dense network","display_name":"Performance analysis of a dual stage deep rain streak removal convolution neural network module with a modified deep residual dense network","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4390303882","doi":"https://doi.org/10.34768/amcs-2022-0009"},"language":"en","primary_location":{"id":"doi:10.34768/amcs-2022-0009","is_oa":true,"landing_page_url":"https://doi.org/10.34768/amcs-2022-0009","pdf_url":"https://sciendo.com/pdf/10.34768/amcs-2022-0009","source":{"id":"https://openalex.org/S117679522","display_name":"International Journal of Applied Mathematics and Computer Science","issn_l":"1641-876X","issn":["1641-876X","2083-8492"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Applied Mathematics and Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://sciendo.com/pdf/10.34768/amcs-2022-0009","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045814816","display_name":"Thiyagarajan Jayaraman","orcid":"https://orcid.org/0000-0002-5382-2118"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Thiyagarajan Jayaraman","raw_affiliation_strings":["Department of Mechatronics Engineering, Sona College of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechatronics Engineering, Sona College of Technology","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046737092","display_name":"Gowri Shankar Chinnusamy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gowri Shankar Chinnusamy","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, KSR College of Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, KSR College of Engineering","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5045814816"],"corresponding_institution_ids":[],"apc_list":{"value":4080,"currency":"PLN","value_usd":1100},"apc_paid":{"value":4080,"currency":"PLN","value_usd":1100},"fwci":0.6852,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.70409152,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"32","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9922000169754028,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9922000169754028,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9842000007629395,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T10511","display_name":"High voltage insulation and dielectric phenomena","score":0.9786999821662903,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.774653434753418},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6441210508346558},{"id":"https://openalex.org/keywords/streak","display_name":"Streak","score":0.5982111096382141},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.5941102504730225},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5506057143211365},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4723915755748749},{"id":"https://openalex.org/keywords/stage","display_name":"Stage (stratigraphy)","score":0.4308525323867798},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.420790433883667},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38796180486679077},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.32913118600845337},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2129790186882019},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.20361223816871643},{"id":"https://openalex.org/keywords/mineralogy","display_name":"Mineralogy","score":0.1683354675769806},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.16238611936569214}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.774653434753418},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6441210508346558},{"id":"https://openalex.org/C65185188","wikidata":"https://www.wikidata.org/wiki/Q107775","display_name":"Streak","level":2,"score":0.5982111096382141},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.5941102504730225},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5506057143211365},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4723915755748749},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.4308525323867798},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.420790433883667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38796180486679077},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.32913118600845337},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2129790186882019},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.20361223816871643},{"id":"https://openalex.org/C199289684","wikidata":"https://www.wikidata.org/wiki/Q83353","display_name":"Mineralogy","level":1,"score":0.1683354675769806},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.16238611936569214},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.34768/amcs-2022-0009","is_oa":true,"landing_page_url":"https://doi.org/10.34768/amcs-2022-0009","pdf_url":"https://sciendo.com/pdf/10.34768/amcs-2022-0009","source":{"id":"https://openalex.org/S117679522","display_name":"International Journal of Applied Mathematics and Computer Science","issn_l":"1641-876X","issn":["1641-876X","2083-8492"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Applied Mathematics and Computer Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:3b681603718142ffa036cee3b6ac0699","is_oa":true,"landing_page_url":"https://doaj.org/article/3b681603718142ffa036cee3b6ac0699","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Applied Mathematics and Computer Science, Vol 32, Iss 1, Pp 111-123 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.34768/amcs-2022-0009","is_oa":true,"landing_page_url":"https://doi.org/10.34768/amcs-2022-0009","pdf_url":"https://sciendo.com/pdf/10.34768/amcs-2022-0009","source":{"id":"https://openalex.org/S117679522","display_name":"International Journal of Applied Mathematics and Computer Science","issn_l":"1641-876X","issn":["1641-876X","2083-8492"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Applied Mathematics and Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4390303882.pdf"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W634211087","https://openalex.org/W1529155083","https://openalex.org/W1976056157","https://openalex.org/W2046119925","https://openalex.org/W2054604489","https://openalex.org/W2119535410","https://openalex.org/W2121396509","https://openalex.org/W2133665775","https://openalex.org/W2141983208","https://openalex.org/W2159269332","https://openalex.org/W2194775991","https://openalex.org/W2466666260","https://openalex.org/W2559264300","https://openalex.org/W2740982616","https://openalex.org/W2780930362","https://openalex.org/W2884068670","https://openalex.org/W2909678128","https://openalex.org/W2912435603","https://openalex.org/W2913360047","https://openalex.org/W2933291251","https://openalex.org/W2952029912","https://openalex.org/W2963446712","https://openalex.org/W2964101377","https://openalex.org/W2964212750","https://openalex.org/W2975763800","https://openalex.org/W2982612225","https://openalex.org/W2998799945","https://openalex.org/W3003938523","https://openalex.org/W3026432413","https://openalex.org/W3026513441","https://openalex.org/W3026656919","https://openalex.org/W3031753296","https://openalex.org/W3043278356","https://openalex.org/W3047572483","https://openalex.org/W3083144531","https://openalex.org/W3089070259","https://openalex.org/W3109720342","https://openalex.org/W3188337644","https://openalex.org/W4295312788"],"related_works":["https://openalex.org/W2082818786","https://openalex.org/W2000740899","https://openalex.org/W2371834895","https://openalex.org/W2997637732","https://openalex.org/W2381571063","https://openalex.org/W213647845","https://openalex.org/W2047678803","https://openalex.org/W2362555026","https://openalex.org/W4255171458","https://openalex.org/W2964954556"],"abstract_inverted_index":{"The":[0,21,55,108,141,159,205],"visual":[1,29],"appearance":[2],"of":[3,31,57,116,150,161],"outdoor":[4],"captured":[5],"images":[6,218],"is":[7,64,71,144,166],"affected":[8],"by":[9],"various":[10,151],"weather":[11,42],"conditions,":[12],"such":[13,180],"as":[14,181],"rain":[15,22,46,58,77,81,102],"patterns,":[16],"haze,":[17],"fog":[18],"and":[19,51,68,93,133,172,198,216,235],"snow.":[20],"pattern":[23,47,78],"creates":[24],"more":[25,69],"degradation":[26],"in":[27,154],"the":[28,32,45,75,88,98,148,155,162,169,182,187,192,199,210,229],"quality":[30,202],"image":[33,53,63,90,111,201,224],"due":[34],"to":[35,73,146],"its":[36],"physical":[37],"structure":[38],"compared":[39,219],"with":[40,220],"other":[41,221],"conditions.":[43],"Also,":[44],"affects":[48],"both":[49,214],"foreground":[50],"background":[52],"information.":[54],"removal":[56,104,123],"patterns":[59],"from":[60,79],"a":[61,65,95,120,127,134],"single":[62,89,110,223],"critical":[66],"process,":[67],"attention":[70],"given":[72],"remove":[74],"structural":[76,193],"real-time":[80,173,217],"images.":[82],"In":[83,227],"this":[84],"paper,":[85],"we":[86],"analyze":[87],"deraining":[91,112,157,164,225],"problem":[92],"present":[94],"solution":[96],"using":[97,176],"dual":[99],"stage":[100],"deep":[101],"streak":[103],"convolutional":[105],"neural":[106],"network.":[107,158],"proposed":[109,156,163,211,230],"framework":[113,212],"primarily":[114],"consists":[115],"three":[117],"main":[118],"blocks:":[119],"derain":[121],"streaks":[122],"CNN":[124],"(derain":[125],"SRCNN),":[126],"modified":[128],"residual":[129],"dense":[130],"block":[131],"(MRDB),":[132],"six-stage":[135],"scale":[136],"feature":[137,188],"aggregation":[138],"module":[139],"(3SFAM).":[140],"ablation":[142],"study":[143],"conducted":[145],"evaluate":[147],"performance":[149,178],"modules":[152],"available":[153],"robustness":[160],"network":[165,231],"evaluated":[167],"over":[168],"popular":[170],"synthetic":[171,215],"data":[174],"sets":[175],"four":[177],"metrics":[179],"peak":[183],"signal-to-noise":[184],"ratio":[185],"(PSNR),":[186],"similarity":[189,194],"index":[190,195,203],"(FSIM),":[191],"measure":[196],"(SSIM),":[197],"universal":[200],"(UIQI).":[204],"experimental":[206],"results":[207],"show":[208],"that":[209],"outperforms":[213],"state-of-the-art":[222],"approaches.":[226],"addition,":[228],"takes":[232],"less":[233],"running":[234],"training":[236],"time.":[237]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-01-20T17:24:06.736184","created_date":"2023-12-29T00:00:00"}
