{"id":"https://openalex.org/W7140215034","doi":"https://doi.org/10.48550/arxiv.2603.20868","title":"TAFG-MAN: Timestep-Adaptive Frequency-Gated Latent Diffusion for Efficient and High-Quality Low-Dose CT Image Denoising","display_name":"TAFG-MAN: Timestep-Adaptive Frequency-Gated Latent Diffusion for Efficient and High-Quality Low-Dose CT Image Denoising","publication_year":2026,"publication_date":"2026-03-21","ids":{"openalex":"https://openalex.org/W7140215034","doi":"https://doi.org/10.48550/arxiv.2603.20868"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.20868","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20868","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.20868","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Fang, Tangtangfang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Tangtangfang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Jiao, Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiao, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"He, Xiangjian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Xiangjian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Hu, Jingxi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Jingxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Yang, Jiaqi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jiaqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.5386999845504761,"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":0.5386999845504761,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.24120000004768372,"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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.044599998742341995,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.7578999996185303},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6291000247001648},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5600000023841858},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5281999707221985},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.45579999685287476},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.3725999891757965},{"id":"https://openalex.org/keywords/image-denoising","display_name":"Image denoising","score":0.36480000615119934},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.3589000105857849}],"concepts":[{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.7578999996185303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6650999784469604},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6291000247001648},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5608999729156494},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5600000023841858},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5281999707221985},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4652999937534332},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.45579999685287476},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.3725999891757965},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.36480000615119934},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.3589000105857849},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.35569998621940613},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C205372313","wikidata":"https://www.wikidata.org/wiki/Q355645","display_name":"Latent image","level":3,"score":0.305400013923645},{"id":"https://openalex.org/C101453961","wikidata":"https://www.wikidata.org/wiki/Q7048948","display_name":"Non-local means","level":4,"score":0.2971000075340271},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.2784999907016754},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C30814859","wikidata":"https://www.wikidata.org/wiki/Q4119603","display_name":"Video denoising","level":5,"score":0.27079999446868896},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2639000117778778},{"id":"https://openalex.org/C128197687","wikidata":"https://www.wikidata.org/wiki/Q5477515","display_name":"Statistical noise","level":2,"score":0.2547999918460846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.20868","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20868","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.20868","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20868","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Low-dose":[0],"computed":[1],"tomography":[2],"(LDCT)":[3],"reduces":[4],"radiation":[5],"exposure":[6],"but":[7],"also":[8],"introduces":[9,114],"substantial":[10],"noise":[11,20,126],"and":[12,38,59,73,84,87,113,128,155,165],"structural":[13,107],"degradation,":[14],"making":[15],"it":[16,150],"difficult":[17],"to":[18],"suppress":[19],"without":[21,148],"erasing":[22],"subtle":[23],"anatomical":[24],"details.":[25],"In":[26,98],"this":[27,99],"paper,":[28],"we":[29],"present":[30],"TAFG-MAN,":[31],"a":[32,46,55,60,136],"latent":[33,51,57],"diffusion":[34,52],"framework":[35,44],"for":[36],"efficient":[37],"high-quality":[39],"LDCT":[40],"image":[41],"denoising.":[42],"The":[43],"combines":[45],"perceptually":[47],"optimized":[48],"autoencoder,":[49],"conditional":[50],"restoration":[53],"in":[54,92],"compact":[56],"space,":[58],"lightweight":[61],"Timestep-Adaptive":[62],"Frequency-Gated":[63],"(TAFG)":[64],"conditioning":[65,174],"design.":[66],"TAFG":[67],"decomposes":[68],"condition":[69],"features":[70],"into":[71],"low-":[72],"high-frequency":[74,90],"components,":[75],"predicts":[76],"timestep-adaptive":[77],"gates":[78],"from":[79],"the":[80,101,123,161,169,172],"current":[81],"denoising":[82,94,120],"feature":[83],"timestep":[85],"embedding,":[86],"progressively":[88],"releases":[89],"guidance":[91,108],"later":[93],"stages":[95],"before":[96],"cross-attention.":[97],"way,":[100],"model":[102],"relies":[103],"more":[104,117],"on":[105],"stable":[106],"at":[109],"early":[110],"reverse":[111],"steps":[112],"fine":[115],"details":[116],"cautiously":[118],"as":[119],"proceeds,":[121],"improving":[122],"balance":[124],"between":[125],"suppression":[127],"detail":[129,153],"preservation.":[130],"Experiments":[131],"show":[132],"that":[133],"TAFG-MAN":[134],"achieves":[135],"favorable":[137],"quality-efficiency":[138],"trade-off":[139],"against":[140],"representative":[141],"baselines.":[142],"Compared":[143],"with":[144],"its":[145],"base":[146],"variant":[147],"TAFG,":[149],"further":[151],"improves":[152],"preservation":[154],"perceptual":[156],"quality":[157],"while":[158],"maintaining":[159],"essentially":[160],"same":[162],"inference":[163],"cost,":[164],"ablation":[166],"results":[167],"confirm":[168],"effectiveness":[170],"of":[171],"proposed":[173],"mechanism.":[175]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-25T00:00:00"}