{"id":"https://openalex.org/W4416666538","doi":"https://doi.org/10.48550/arxiv.2511.16717","title":"A Machine Learning-Driven Solution for Denoising Inertial Confinement Fusion Images","display_name":"A Machine Learning-Driven Solution for Denoising Inertial Confinement Fusion Images","publication_year":2025,"publication_date":"2025-11-20","ids":{"openalex":"https://openalex.org/W4416666538","doi":"https://doi.org/10.48550/arxiv.2511.16717"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2511.16717","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.16717","pdf_url":"https://arxiv.org/pdf/2511.16717","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.16717","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120473706","display_name":"Asya Y. Akkus","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Akkus, Asya Y.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029480218","display_name":"Bradley T. Wolfe","orcid":"https://orcid.org/0000-0002-6830-1614"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wolfe, Bradley T.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104622572","display_name":"P.-H. Chu","orcid":"https://orcid.org/0000-0003-1372-2910"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chu, Pinghan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082565705","display_name":"Chengkun Huang","orcid":"https://orcid.org/0000-0002-3176-8042"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Chengkun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028392023","display_name":"Christopher J. Campbell","orcid":"https://orcid.org/0009-0003-4629-9669"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Campbell, Chris S.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083198695","display_name":"Mariana Alvarado Alvarez","orcid":"https://orcid.org/0009-0007-8358-6711"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alvarez, Mariana Alvarado","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058209953","display_name":"P. L. Volegov","orcid":"https://orcid.org/0000-0003-0361-7500"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Volegov, Petr","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028353918","display_name":"D. N. Fittinghoff","orcid":"https://orcid.org/0000-0002-6089-8911"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fittinghoff, David","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079315014","display_name":"R.E. Reinovsky","orcid":"https://orcid.org/0000-0001-5496-9915"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reinovsky, Robert","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5012391877","display_name":"\u202aZhehui Wang","orcid":"https://orcid.org/0000-0001-7826-4063"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhehui","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5120473706"],"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/T10384","display_name":"Laser-Plasma Interactions and Diagnostics","score":0.6295999884605408,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10384","display_name":"Laser-Plasma Interactions and Diagnostics","score":0.6295999884605408,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11949","display_name":"Nuclear Physics and Applications","score":0.1762000024318695,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11216","display_name":"Radiation Detection and Scintillator Technologies","score":0.03269999846816063,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6794000267982483},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5922999978065491},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5392000079154968},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5325000286102295},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.4426000118255615},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.4424999952316284},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4129999876022339},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4083000123500824},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.37950000166893005},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.37389999628067017}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6958000063896179},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6794000267982483},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5922999978065491},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5741000175476074},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5572999715805054},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5392000079154968},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5325000286102295},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.4426000118255615},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4424999952316284},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4129999876022339},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4083000123500824},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.37950000166893005},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C72659945","wikidata":"https://www.wikidata.org/wiki/Q1503574","display_name":"Shot noise","level":3,"score":0.3596000075340271},{"id":"https://openalex.org/C131720326","wikidata":"https://www.wikidata.org/wiki/Q5196075","display_name":"Curvelet","level":4,"score":0.3337000012397766},{"id":"https://openalex.org/C2778921489","wikidata":"https://www.wikidata.org/wiki/Q1431014","display_name":"National Ignition Facility","level":4,"score":0.33320000767707825},{"id":"https://openalex.org/C203504353","wikidata":"https://www.wikidata.org/wiki/Q4765461","display_name":"Anisotropic diffusion","level":3,"score":0.32330000400543213},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.32030001282691956},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.3005000054836273},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.28610000014305115},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2849000096321106},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C101453961","wikidata":"https://www.wikidata.org/wiki/Q7048948","display_name":"Non-local means","level":4,"score":0.259799987077713}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2511.16717","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.16717","pdf_url":"https://arxiv.org/pdf/2511.16717","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.16717","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.16717","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.16717","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.16717","pdf_url":"https://arxiv.org/pdf/2511.16717","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Neutron":[0],"imaging":[1,151],"is":[2,65],"essential":[3,143],"for":[4,137,192,209],"diagnosing":[5],"and":[6,46,57,73,103,157,169,181],"optimizing":[7],"inertial":[8],"confinement":[9],"fusion":[10],"implosions":[11],"at":[12],"the":[13,19,35,59,62,82,87,91,131,187],"National":[14],"Ignition":[15],"Facility.":[16],"Due":[17],"to":[18,110,135,174],"required":[20],"10-micrometer":[21],"resolution,":[22],"however,":[23],"neutron":[24,150,194],"image":[25,27,93,144],"require":[26],"reconstruction":[28,94,167,207],"using":[29],"iterative":[30,92],"algorithms.":[31],"For":[32],"low-yield":[33],"sources,":[34],"images":[36],"may":[37],"be":[38],"degraded":[39],"by":[40],"various":[41],"types":[42],"of":[43,90,189],"noise.":[44],"Gaussian":[45],"Poisson":[47],"noise":[48,83,140],"often":[49],"coexist":[50],"within":[51],"one":[52],"image,":[53],"obscuring":[54],"fine":[55],"details":[56],"blurring":[58],"edges":[60],"where":[61],"source":[63],"information":[64],"encoded.":[66],"Traditional":[67],"denoising":[68,193],"techniques,":[69],"such":[70,178],"as":[71,179],"filtering":[72,176,183],"thresholding,":[74],"can":[75],"inadvertently":[76],"alter":[77],"critical":[78,200],"features":[79],"or":[80],"reshape":[81],"statistics,":[84],"potentially":[85],"impacting":[86],"ultimate":[88],"fidelity":[89],"pipeline.":[95],"However,":[96],"recent":[97],"advances":[98],"in":[99,130],"synthetic":[100],"data":[101],"production":[102],"machine":[104],"learning":[105,191],"have":[106],"opened":[107],"new":[108],"opportunities":[109],"address":[111],"these":[112],"challenges.":[113],"In":[114],"this":[115,196],"study,":[116],"we":[117],"present":[118],"an":[119],"unsupervised":[120,190],"autoencoder":[121],"with":[122],"a":[123,199],"Cohen-Daubechies-":[124],"Feauveau":[125],"(CDF":[126],"97)":[127],"wavelet":[128],"transform":[129],"latent":[132],"space,":[133],"designed":[134],"suppress":[136],"mixed":[138],"Gaussian-Poisson":[139],"while":[141],"preserving":[142],"features.":[145],"The":[146],"network":[147],"successfully":[148],"denoises":[149],"data.":[152],"Benchmarking":[153],"against":[154],"both":[155],"simulated":[156],"experimental":[158],"NIF":[159],"datasets":[160],"demonstrates":[161],"that":[162],"our":[163],"approach":[164],"achieves":[165],"lower":[166],"error":[168],"superior":[170],"edge":[171],"preservation":[172],"compared":[173],"conventional":[175],"methods":[177],"Block-matching":[180],"3D":[182],"(BM3D).":[184],"By":[185],"validating":[186],"effectiveness":[188],"images,":[195],"study":[197],"establishes":[198],"first":[201],"step":[202],"towards":[203],"fully":[204],"AI-driven,":[205],"end-to-end":[206],"frameworks":[208],"ICF":[210],"diagnostics.":[211]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-25T00:00:00"}