{"id":"https://openalex.org/W7131664033","doi":"https://doi.org/10.48550/arxiv.2602.21987","title":"PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging","display_name":"PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging","publication_year":2026,"publication_date":"2026-02-25","ids":{"openalex":"https://openalex.org/W7131664033","doi":"https://doi.org/10.48550/arxiv.2602.21987"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.21987","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116719439","display_name":"Jitindra Fartiyal","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fartiyal, Jitindra","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126878001","display_name":"Pedro Freire","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Freire, Pedro","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084823616","display_name":"Sergei K. Turitsyn","orcid":"https://orcid.org/0000-0003-0101-3834"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Turitsyn, Sergei K.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126899646","display_name":"Sergei G. Solovski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Solovski, Sergei G.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5116719439"],"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":0.12929999828338623,"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":0.12929999828338623,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.08449999988079071,"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":0.07989999651908875,"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/noise-reduction","display_name":"Noise reduction","score":0.7014999985694885},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5507000088691711},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5281000137329102},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.5169000029563904},{"id":"https://openalex.org/keywords/scanner","display_name":"Scanner","score":0.44670000672340393},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.445499986410141},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.42250001430511475},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4223000109195709},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4221000075340271},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.3677000105381012}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7297999858856201},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.7014999985694885},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6625999808311462},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5759999752044678},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5507000088691711},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5281000137329102},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.5169000029563904},{"id":"https://openalex.org/C2779751349","wikidata":"https://www.wikidata.org/wiki/Q1474480","display_name":"Scanner","level":2,"score":0.44670000672340393},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.445499986410141},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.42250001430511475},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4223000109195709},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4221000075340271},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3677000105381012},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.367000013589859},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.3571000099182129},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.35179999470710754},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.34619998931884766},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.33009999990463257},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3151000142097473},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.30649998784065247},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.27309998869895935},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.26660001277923584},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2590999901294708},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.2583000063896179}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.21987","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.21987","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.21987","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.21987","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.8986151814460754,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Low-dose":[0],"CT":[1,140],"images":[2],"are":[3],"essential":[4],"for":[5,199],"reducing":[6],"radiation":[7],"exposure":[8],"in":[9,150,159],"cancer":[10],"screening,":[11],"pediatric":[12],"imaging,":[13],"and":[14,38,46,58,92,114,124,131,147,152,164,172,177,195,206],"longitudinal":[15],"monitoring":[16],"protocols,":[17],"but":[18],"their":[19],"quality":[20],"is":[21,118,155],"often":[22,44],"degraded":[23],"by":[24,175,182],"noise":[25,108],"from":[26],"low-dose":[27],"acquisition,":[28],"patient":[29],"motion,":[30],"or":[31,66],"scanner":[32],"limitations,":[33],"affecting":[34],"both":[35],"clinical":[36,73,207],"interpretation":[37],"downstream":[39],"analysis.":[40],"Traditional":[41],"filtering":[42],"approaches":[43],"over-smooth":[45],"lose":[47],"fine":[48,112],"anatomical":[49,115],"details,":[50],"while":[51,110],"deep":[52],"learning":[53],"methods,":[54],"including":[55],"CNNs,":[56],"GANs,":[57],"transformers,":[59],"may":[60],"struggle":[61],"to":[62,157],"preserve":[63],"such":[64],"details":[65],"require":[67],"large,":[68],"computationally":[69,196],"expensive":[70],"models,":[71],"limiting":[72],"practicality.":[74],"We":[75],"propose":[76],"PatchDenoiser,":[77],"a":[78,98,192],"lightweight,":[79],"energy-efficient":[80],"multi-scale":[81],"patch-based":[82],"denoising":[83,87],"framework.":[84],"It":[85,154],"decomposes":[86],"into":[88],"local":[89],"texture":[90],"extraction":[91],"global":[93],"context":[94],"aggregation,":[95],"fused":[96],"via":[97],"spatially":[99],"aware":[100],"patch":[101],"fusion":[102],"strategy.":[103],"This":[104],"design":[105],"enables":[106],"effective":[107],"suppression":[109],"preserving":[111],"structural":[113],"details.":[116],"PatchDenoiser":[117,142,189],"ultra-lightweight,":[119],"with":[120,185],"far":[121],"fewer":[122],"parameters":[123,174],"lower":[125],"computational":[126],"complexity":[127],"than":[128],"CNN,":[129],"GAN,":[130],"transformer":[132],"based":[133],"denoisers.":[134,188],"On":[135],"the":[136],"2016":[137],"Mayo":[138],"Low-Dose":[139],"dataset,":[141],"consistently":[143],"outperforms":[144],"state-of-the-art":[145],"CNN-":[146],"GAN-based":[148],"methods":[149],"PSNR":[151],"SSIM.":[153],"robust":[156],"variations":[158],"slice":[160],"thickness,":[161],"reconstruction":[162],"kernels,":[163],"HU":[165],"windows,":[166],"generalizes":[167],"across":[168],"scanners":[169],"without":[170],"fine-tuning,":[171],"reduces":[173],"~9x":[176],"energy":[178],"consumption":[179],"per":[180],"inference":[181],"~27x":[183],"compared":[184],"conventional":[186],"CNN":[187],"thus":[190],"provides":[191],"practical,":[193],"scalable,":[194],"efficient":[197],"solution":[198],"medical":[200],"image":[201],"denoising,":[202],"balancing":[203],"performance,":[204],"robustness,":[205],"deployability.":[208]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-27T00:00:00"}