{"id":"https://openalex.org/W4416251849","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228631","title":"FD-GAN: A Dual-Domain Approach with Fourier Domain Discriminators for Denoising Low-Dose CT Images","display_name":"FD-GAN: A Dual-Domain Approach with Fourier Domain Discriminators for Denoising Low-Dose CT Images","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251849","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228631"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228631","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228631","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5119780419","display_name":"Jiashu Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153668","display_name":"Wenzhou-Kean University","ror":"https://ror.org/05609xa16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153668"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiashu Li","raw_affiliation_strings":["Wenzhou-Kean University,Department of Mathematical Sciences,Wenzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wenzhou-Kean University,Department of Mathematical Sciences,Wenzhou,China","institution_ids":["https://openalex.org/I4210153668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102844828","display_name":"Yuangen Mi","orcid":"https://orcid.org/0009-0008-7477-0960"},"institutions":[{"id":"https://openalex.org/I4210153668","display_name":"Wenzhou-Kean University","ror":"https://ror.org/05609xa16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153668"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yixuan Mi","raw_affiliation_strings":["Wenzhou-Kean University,Department of Computer Science,Wenzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wenzhou-Kean University,Department of Computer Science,Wenzhou,China","institution_ids":["https://openalex.org/I4210153668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059135009","display_name":"Moyang Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153668","display_name":"Wenzhou-Kean University","ror":"https://ror.org/05609xa16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153668"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Moyang Chen","raw_affiliation_strings":["Wenzhou-Kean University,Department of Mathematical Sciences,Wenzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wenzhou-Kean University,Department of Mathematical Sciences,Wenzhou,China","institution_ids":["https://openalex.org/I4210153668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076695361","display_name":"Kuan Huang","orcid":"https://orcid.org/0000-0001-5710-1118"},"institutions":[{"id":"https://openalex.org/I47449453","display_name":"Kean University","ror":"https://ror.org/04wzzqn13","country_code":"US","type":"education","lineage":["https://openalex.org/I47449453"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kuan Huang","raw_affiliation_strings":["Kean University,Department of Computer Science,Union,NJ,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kean University,Department of Computer Science,Union,NJ,United States","institution_ids":["https://openalex.org/I47449453"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101958358","display_name":"Gaurav Gupta","orcid":"https://orcid.org/0000-0002-2944-425X"},"institutions":[{"id":"https://openalex.org/I4210153668","display_name":"Wenzhou-Kean University","ror":"https://ror.org/05609xa16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153668"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gaurav Gupta","raw_affiliation_strings":["Wenzhou-Kean University,Department of Mathematical Sciences,Wenzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wenzhou-Kean University,Department of Mathematical Sciences,Wenzhou,China","institution_ids":["https://openalex.org/I4210153668"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069466656","display_name":"Guanchao Tong","orcid":"https://orcid.org/0000-0002-3503-2745"},"institutions":[{"id":"https://openalex.org/I4210153668","display_name":"Wenzhou-Kean University","ror":"https://ror.org/05609xa16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153668"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanchao Tong","raw_affiliation_strings":["Wenzhou-Kean University,Department of Mathematical Sciences,Wenzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wenzhou-Kean University,Department of Mathematical Sciences,Wenzhou,China","institution_ids":["https://openalex.org/I4210153668"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.44857233,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.6726999878883362,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.6726999878883362,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.0869000032544136,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10688","display_name":"Image and Signal Denoising Methods","score":0.05640000104904175,"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/robustness","display_name":"Robustness (evolution)","score":0.7197999954223633},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6276000142097473},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.5892000198364258},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.49939998984336853},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48829999566078186},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4832000136375427},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.4715999960899353},{"id":"https://openalex.org/keywords/fourier-domain","display_name":"Fourier domain","score":0.45339998602867126},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4453999996185303},{"id":"https://openalex.org/keywords/image-denoising","display_name":"Image denoising","score":0.42010000348091125}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7215999960899353},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7197999954223633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6686999797821045},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6276000142097473},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.5892000198364258},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.49939998984336853},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48829999566078186},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4832000136375427},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.4715999960899353},{"id":"https://openalex.org/C3019555358","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Fourier domain","level":3,"score":0.45339998602867126},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4453999996185303},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.42010000348091125},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4187000095844269},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.382999986410141},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.3801000118255615},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36890000104904175},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36250001192092896},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3601999878883362},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.30219998955726624},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.2946999967098236},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.27480000257492065},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.27320000529289246},{"id":"https://openalex.org/C30814859","wikidata":"https://www.wikidata.org/wiki/Q4119603","display_name":"Video denoising","level":5,"score":0.272599995136261},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.25769999623298645},{"id":"https://openalex.org/C101453961","wikidata":"https://www.wikidata.org/wiki/Q7048948","display_name":"Non-local means","level":4,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228631","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228631","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":32,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2119259145","https://openalex.org/W2133665775","https://openalex.org/W2508457857","https://openalex.org/W2527841944","https://openalex.org/W2574952845","https://openalex.org/W2584483805","https://openalex.org/W2593414223","https://openalex.org/W2593557470","https://openalex.org/W2743780012","https://openalex.org/W2748739903","https://openalex.org/W2787564202","https://openalex.org/W2904386749","https://openalex.org/W2919234133","https://openalex.org/W2953327099","https://openalex.org/W2980780523","https://openalex.org/W3035687950","https://openalex.org/W3095556331","https://openalex.org/W3098281398","https://openalex.org/W3103645830","https://openalex.org/W3166727904","https://openalex.org/W3182469500","https://openalex.org/W3188999719","https://openalex.org/W3202941857","https://openalex.org/W3213236098","https://openalex.org/W4214669209","https://openalex.org/W4322155345","https://openalex.org/W4367316088","https://openalex.org/W4388125074","https://openalex.org/W4390466028","https://openalex.org/W4403069616","https://openalex.org/W4403768124"],"related_works":[],"abstract_inverted_index":{"Low-Dose":[0],"CT":[1],"(LDCT)":[2],"denoising":[3,172],"is":[4],"critical":[5],"for":[6,169],"minimizing":[7],"radiation":[8],"exposure":[9],"while":[10],"maintaining":[11],"diagnostic":[12],"image":[13,71],"quality.":[14,152],"Conventional":[15],"model-based":[16],"and":[17,50,72,79,94,113,121,142,150,160],"transform":[18],"domain":[19,81,115],"approaches":[20],"often":[21],"result":[22],"in":[23,68,98,146],"over-smoothed":[24],"images":[25],"with":[26,111],"diminished":[27],"structural":[28,51],"details.":[29],"To":[30],"overcome":[31],"these":[32],"limitations,":[33],"deep":[34],"learning":[35],"techniques":[36],"have":[37],"emerged":[38],"as":[39,88],"powerful":[40],"alternatives,":[41],"leveraging":[42],"data-driven":[43],"models":[44,145],"to":[45,117],"achieve":[46],"superior":[47],"noise":[48],"suppression":[49],"preservation.":[52],"In":[53],"this":[54],"work,":[55],"we":[56],"propose":[57],"a":[58,166],"novel":[59],"model":[60],"named":[61],"FD-GAN":[62,83,135,154],"that":[63,134],"incorporates":[64],"dual":[65],"discriminators":[66],"operating":[67],"both":[69,147],"the":[70,128],"Fourier":[73,80,114],"domains.":[74],"By":[75],"jointly":[76],"optimizing":[77],"spatial":[78],"information,":[82],"addresses":[84],"common":[85],"issues":[86],"such":[87],"over-smoothing,":[89],"loss":[90,110],"of":[91],"high-frequency":[92],"details,":[93],"training":[95,159],"instability":[96],"observed":[97],"traditional":[99],"Generative":[100],"Adversarial":[101],"Network":[102],"(GAN)":[103],"models.":[104],"Our":[105],"framework":[106],"integrates":[107],"pixel-wise":[108],"reconstruction":[109],"adversarial":[112],"losses":[116],"ensure":[118],"global":[119],"consistency":[120],"local":[122],"detail":[123],"enhancement.":[124],"Extensive":[125],"experiments":[126],"on":[127],"NIHAAPM-Mayo":[129],"Clinic":[130],"LDCT":[131,171],"dataset":[132],"demonstrate":[133],"outperforms":[136],"conventional":[137],"Mean":[138],"Squared":[139],"Error":[140],"(MSE)-based":[141],"single-domain":[143],"GAN":[144],"quantitative":[148],"metrics":[149],"visual":[151],"Furthermore,":[153],"offers":[155],"improved":[156],"stability":[157],"during":[158],"robustness":[161],"against":[162],"artifacts,":[163],"making":[164],"it":[165],"promising":[167],"solution":[168],"clinical":[170],"applications.":[173]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-14T00:00:00"}