{"id":"https://openalex.org/W4406266184","doi":"https://doi.org/10.1109/gcce62371.2024.10760976","title":"A Deep Learning-based Deraining Approach using Frequency Domain Loss","display_name":"A Deep Learning-based Deraining Approach using Frequency Domain Loss","publication_year":2024,"publication_date":"2024-10-29","ids":{"openalex":"https://openalex.org/W4406266184","doi":"https://doi.org/10.1109/gcce62371.2024.10760976"},"language":"en","primary_location":{"id":"doi:10.1109/gcce62371.2024.10760976","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce62371.2024.10760976","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 13th Global Conference on Consumer Electronics (GCCE)","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/A5053069251","display_name":"Yusuke Yamamoto","orcid":"https://orcid.org/0000-0001-9829-6521"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yusuke Yamamoto","raw_affiliation_strings":["Information Science and Engineering Ritsumeikan Univ.,Osaka,Japan"],"affiliations":[{"raw_affiliation_string":"Information Science and Engineering Ritsumeikan Univ.,Osaka,Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010150695","display_name":"Yinhao Li","orcid":"https://orcid.org/0000-0002-8924-5279"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yinhao Li","raw_affiliation_strings":["Information Science and Engineering Ritsumeikan Univ.,Osaka,Japan"],"affiliations":[{"raw_affiliation_string":"Information Science and Engineering Ritsumeikan Univ.,Osaka,Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016673272","display_name":"Hiroshi Taga","orcid":null},"institutions":[{"id":"https://openalex.org/I4210118089","display_name":"Mitsui (Japan)","ror":"https://ror.org/01qw4g764","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210118089"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroshi Taga","raw_affiliation_strings":["Mitsui E&#x0026;S System Research Inc.,DX Promotion Department,Chiba,Japan"],"affiliations":[{"raw_affiliation_string":"Mitsui E&#x0026;S System Research Inc.,DX Promotion Department,Chiba,Japan","institution_ids":["https://openalex.org/I4210118089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008898350","display_name":"Koki Iwasa","orcid":null},"institutions":[{"id":"https://openalex.org/I4210118089","display_name":"Mitsui (Japan)","ror":"https://ror.org/01qw4g764","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210118089"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Koki Iwasa","raw_affiliation_strings":["Mitsui E&#x0026;S System Research Inc.,DX Promotion Department,Chiba,Japan"],"affiliations":[{"raw_affiliation_string":"Mitsui E&#x0026;S System Research Inc.,DX Promotion Department,Chiba,Japan","institution_ids":["https://openalex.org/I4210118089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115842083","display_name":"Ryuichi Shichikawa","orcid":null},"institutions":[{"id":"https://openalex.org/I4210118089","display_name":"Mitsui (Japan)","ror":"https://ror.org/01qw4g764","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210118089"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryuichi Shichikawa","raw_affiliation_strings":["Mitsui E&#x0026;S System Research Inc.,DX Promotion Department,Chiba,Japan"],"affiliations":[{"raw_affiliation_string":"Mitsui E&#x0026;S System Research Inc.,DX Promotion Department,Chiba,Japan","institution_ids":["https://openalex.org/I4210118089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115842084","display_name":"Makoto Suganami","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Makoto Suganami","raw_affiliation_strings":["Mitsui E&#x0026;S System Research Inc.,Industry System Solutions Division,Chiba,Japan"],"affiliations":[{"raw_affiliation_string":"Mitsui E&#x0026;S System Research Inc.,Industry System Solutions Division,Chiba,Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109041326","display_name":"Kazuhiro Nakamoto","orcid":null},"institutions":[{"id":"https://openalex.org/I4210118089","display_name":"Mitsui (Japan)","ror":"https://ror.org/01qw4g764","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210118089"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuhiro Nakamoto","raw_affiliation_strings":["Mitsui E&#x0026;S System Research Inc.,DX Promotion Department,Chiba,Japan"],"affiliations":[{"raw_affiliation_string":"Mitsui E&#x0026;S System Research Inc.,DX Promotion Department,Chiba,Japan","institution_ids":["https://openalex.org/I4210118089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101700166","display_name":"Yen-Wei Chen","orcid":"https://orcid.org/0000-0002-8740-0351"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yen-Wei Chen","raw_affiliation_strings":["Information Science and Engineering Ritsumeikan Univ.,Osaka,Japan"],"affiliations":[{"raw_affiliation_string":"Information Science and Engineering Ritsumeikan Univ.,Osaka,Japan","institution_ids":["https://openalex.org/I135768898"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5053069251"],"corresponding_institution_ids":["https://openalex.org/I135768898"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13609303,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"713","last_page":"716"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.954800009727478,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.954800009727478,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9447000026702881,"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":0.6592088937759399},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.47613075375556946},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.4646069407463074},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40213850140571594},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13406190276145935},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.08992123603820801}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6592088937759399},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.47613075375556946},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.4646069407463074},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40213850140571594},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13406190276145935},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.08992123603820801},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/gcce62371.2024.10760976","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce62371.2024.10760976","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 13th Global Conference on Consumer Electronics (GCCE)","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":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Rain":[0],"streak":[1,21,39,78,90,122],"noise":[2,22,40,79,91,123],"degrades":[3],"image":[4,12,27,135],"quality,":[5],"making":[6],"its":[7],"removal":[8],"essential":[9],"for":[10],"restoring":[11],"clarity.":[13],"Deep":[14],"learning-based":[15],"methods":[16],"can":[17],"effectively":[18,115],"remove":[19],"rain":[20,38,77,89,121],"by":[23,124],"learning":[24,63],"from":[25],"public":[26],"datasets":[28],"with":[29,75],"many":[30],"raining":[31],"and":[32,136],"clear":[33],"data":[34],"pairs.":[35],"However,":[36],"since":[37],"affects":[41,92],"the":[42,59,67,117,126,129,132,137,144,165,171],"frequency":[43,70,94,159],"components":[44,55],"of":[45,61,120,131,173],"an":[46],"image,":[47],"it":[48],"is":[49,153],"necessary":[50],"to":[51,56,114,142,155],"focus":[52,143],"on":[53,69,97,147],"these":[54,98],"further":[57],"enhance":[58],"performance":[60],"deep":[62],"methods.":[64],"To":[65],"investigate":[66],"impact":[68,119],"components,":[71],"we":[72,100],"analyzed":[73],"images":[74],"added":[76],"using":[80],"Discrete":[81],"Cosine":[82],"Transform":[83],"(DCT).":[84],"This":[85,110],"analysis":[86],"reveals":[87],"that":[88,164],"specific":[93,148],"components.":[95,160],"Based":[96],"findings,":[99],"propose":[101],"a":[102,150],"new":[103],"loss":[104,111,145,167],"function":[105,112,146,168],"called":[106],"\"Frequency":[107],"Domain":[108],"Loss.\"":[109],"aims":[113],"eliminate":[116],"frequency-based":[118],"calculating":[125],"difference":[127],"between":[128],"DCT":[130],"network":[133],"output":[134],"ground":[138],"truth":[139],"image.":[140],"Additionally,":[141],"differences,":[149],"cropping":[151],"operation":[152],"introduced":[154],"extract":[156],"only":[157],"certain":[158],"Experimental":[161],"results":[162],"demonstrate":[163],"proposed":[166],"significantly":[169],"improves":[170],"quality":[172],"restored":[174],"images.":[175]},"counts_by_year":[],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}
