{"id":"https://openalex.org/W4395474103","doi":"https://doi.org/10.48550/arxiv.2404.15231","title":"Direct Zernike Coefficient Prediction from Point Spread Functions and\n Extended Images using Deep Learning","display_name":"Direct Zernike Coefficient Prediction from Point Spread Functions and\n Extended Images using Deep Learning","publication_year":2024,"publication_date":"2024-04-23","ids":{"openalex":"https://openalex.org/W4395474103","doi":"https://doi.org/10.48550/arxiv.2404.15231"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2404.15231","pdf_url":"https://arxiv.org/pdf/2404.15231","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2404.15231","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076281097","display_name":"Yong En Kok","orcid":"https://orcid.org/0000-0002-5620-9263"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kok, Yong En","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081499382","display_name":"Alexander Bentley","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bentley, Alexander","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028348849","display_name":"Andrew Parkes","orcid":"https://orcid.org/0000-0002-3097-0644"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Parkes, Andrew","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064089698","display_name":"Amanda J. Wright","orcid":"https://orcid.org/0000-0002-4866-5699"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wright, Amanda J.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042224570","display_name":"Michael G. Somekh","orcid":"https://orcid.org/0000-0001-8082-5915"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Somekh, Michael G.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5072892248","display_name":"Michael P. Pound","orcid":"https://orcid.org/0000-0002-5016-1078"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pound, Michael","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"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":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":86},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11325","display_name":"Inertial Navigation Systems and Sensor Fusion Techniques","score":0.9638,"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/T11325","display_name":"Inertial Navigation Systems and Sensor Fusion Techniques","score":0.9638,"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/T10271","display_name":"Seismic Waveform Inversion in Geophysics","score":0.9327,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/attitude-estimation","display_name":"Attitude Estimation","score":0.519966},{"id":"https://openalex.org/keywords/geophysical-imaging","display_name":"Geophysical Imaging","score":0.504041},{"id":"https://openalex.org/keywords/elastic-properties","display_name":"Elastic Properties","score":0.501241}],"concepts":[{"id":"https://openalex.org/C92423082","wikidata":"https://www.wikidata.org/wiki/Q132146","display_name":"Zernike polynomials","level":3,"score":0.93207073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6479163},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.59344065},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5430474},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48015612},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42934057},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29688793},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.1911228},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.15839404},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.12593472},{"id":"https://openalex.org/C165699331","wikidata":"https://www.wikidata.org/wiki/Q461533","display_name":"Wavefront","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2404.15231","pdf_url":"https://arxiv.org/pdf/2404.15231","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2404.15231","pdf_url":"https://arxiv.org/pdf/2404.15231","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4360585206","https://openalex.org/W4323565446","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W4213079790","https://openalex.org/W3215138031","https://openalex.org/W3082895349","https://openalex.org/W3009238340","https://openalex.org/W2731899572","https://openalex.org/W2248239756"],"abstract_inverted_index":{"Optical":[0],"imaging":[1],"quality":[2],"can":[3,152],"be":[4,153],"severely":[5],"degraded":[6],"by":[7,44],"system":[8],"and":[9,27,97,162,167],"sample":[10],"induced":[11],"aberrations.":[12],"Existing":[13],"adaptive":[14],"optics":[15],"systems":[16],"typically":[17],"rely":[18],"on":[19,61,114,186],"iterative":[20],"search":[21],"algorithm":[22],"to":[23,39,52,76],"correct":[24],"for":[25,170,184],"aberrations":[26],"improve":[28],"images.":[29,56],"This":[30,160],"study":[31],"demonstrates":[32],"the":[33,41,47,72,80,99,115,172,182],"application":[34],"of":[35,74,105,111,135,158],"convolutional":[36],"neural":[37],"networks":[38],"characterise":[40],"optical":[42,55],"aberration":[43,173],"directly":[45,122],"predicting":[46,171],"Zernike":[48,83,124],"coefficients":[49],"from":[50],"two":[51],"three":[53,91,176],"phase-diverse":[54,92,179],"We":[57,138],"evaluated":[58],"our":[59],"network":[60],"600,000":[62],"simulated":[63,116,126],"Point":[64],"Spread":[65],"Function":[66],"(PSF)":[67],"datasets":[68],"randomly":[69],"generated":[70],"within":[71],"range":[73],"-1":[75],"1":[77,106],"radians":[78,113],"using":[79,89,145,175],"first":[81],"25":[82],"coefficients.":[84],"The":[85],"results":[86],"show":[87],"that":[88,140],"only":[90,146],"images":[93],"captured":[94],"above,":[95],"below":[96],"at":[98],"focal":[100],"plane":[101],"with":[102],"an":[103],"amplitude":[104],"achieves":[107],"a":[108,132,147,155],"low":[109],"RMSE":[110,134],"0.10":[112],"PSF":[117],"dataset.":[118,188],"Furthermore,":[119],"this":[120,141],"approach":[121,142],"predicts":[123],"modes":[125],"extended":[127],"2D":[128],"samples,":[129],"while":[130],"maintaining":[131],"comparable":[133],"0.15":[136],"radians.":[137],"demonstrate":[139],"is":[143],"effective":[144],"single":[148],"prediction":[149],"step,":[150],"or":[151,177],"iterated":[154],"small":[156],"number":[157],"times.":[159],"simple":[161],"straightforward":[163],"technique":[164],"provides":[165],"rapid":[166],"accurate":[168],"method":[169],"correction":[174],"less":[178],"images,":[180],"paving":[181],"way":[183],"evaluation":[185],"real-world":[187]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4395474103","counts_by_year":[],"updated_date":"2024-10-20T16:08:11.564515","created_date":"2024-04-26"}