乐胖代购免代理版

{"id":"https://openalex.org/W4307005500","doi":"https://doi.org/10.48550/arxiv.2210.10050","title":"Explainable bilevel optimization: an application to the Helsinki deblur challenge","display_name":"Explainable bilevel optimization: an application to the Helsinki deblur challenge","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4307005500","doi":"https://doi.org/10.48550/arxiv.2210.10050"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.10050","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":["Cornell University"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2210.10050","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005747047","display_name":"Silvia Bonettini","orcid":"https://orcid.org/0000-0003-2936-365X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bonettini, Silvia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017595816","display_name":"Giorgia Franchini","orcid":"https://orcid.org/0000-0001-9082-8087"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Franchini, Giorgia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034815145","display_name":"Danilo Pezzi","orcid":"https://orcid.org/0000-0001-7396-9806"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pezzi, Danilo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5024279313","display_name":"Marco Prato","orcid":"https://orcid.org/0000-0002-7327-3347"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prato, Marco","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":60},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9962,"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"}},"topics":[{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9962,"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":0.9897,"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/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9756,"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/deblurring","display_name":"Deblurring","score":0.9368582},{"id":"https://openalex.org/keywords/bilevel-optimization","display_name":"Bilevel optimization","score":0.6120201},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.5011666}],"concepts":[{"id":"https://openalex.org/C2777693668","wikidata":"https://www.wikidata.org/wiki/Q25053743","display_name":"Deblurring","level":5,"score":0.9368582},{"id":"https://openalex.org/C3309286","wikidata":"https://www.wikidata.org/wiki/Q4907693","display_name":"Bilevel optimization","level":3,"score":0.6120201},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5559212},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.54777545},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.5283578},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5223221},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.5011666},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.50087},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46247992},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.42247602},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39322278},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.37832654},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33899266},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3095251},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.24681547},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.20511827},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.10050","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":["Cornell University"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.10050","pdf_url":"http://arxiv.org/pdf/2210.10050","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},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2210.10050","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.10050","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":["Cornell University"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","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/W4297925816","https://openalex.org/W3090200643","https://openalex.org/W2890257643","https://openalex.org/W2602322400","https://openalex.org/W255338418","https://openalex.org/W2340378315","https://openalex.org/W2161149697","https://openalex.org/W2013038594","https://openalex.org/W1604175135","https://openalex.org/W1495503294"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3],"present":[4],"a":[5,13,20,27,33,91,129,138,142,164],"bilevel":[6],"optimization":[7,119,185],"scheme":[8,30],"for":[9,58,85],"the":[10,45,50,59,86,99,114,118,150,155,176,184],"solution":[11],"of":[12,44,67,80,94,101,173,175,186,188],"general":[14],"image":[15,37,88],"deblurring":[16],"problem,":[17],"in":[18,64,90],"which":[19,65,182],"parametric":[21],"variational-like":[22],"approach":[23,167],"is":[24],"encapsulated":[25],"within":[26],"machine":[28,51,145],"learning":[29,52,125,179],"to":[31,71,98,163],"provide":[32],"high":[34],"quality":[35],"reconstructed":[36,87],"with":[38,77,161,171],"automatically":[39],"learned":[40,135],"parameters.":[41,189],"The":[42,111],"ingredients":[43],"variational":[46,115,166],"lower":[47],"level":[48],"and":[49,105,117,131,168],"upper":[53],"one":[54],"are":[55,69,134],"specifically":[56],"chosen":[57],"Helsinki":[60],"Deblur":[61],"Challenge":[62],"2021,":[63],"sequences":[66],"letters":[68],"asked":[70],"be":[72],"recovered":[73],"from":[74],"out-of-focus":[75],"photographs":[76],"increasing":[78],"levels":[79],"blur.":[81],"Our":[82],"proposed":[83,177],"procedure":[84],"consists":[89],"fixed":[92],"number":[93],"FISTA":[95],"iterations":[96],"applied":[97],"minimization":[100],"an":[102],"edge":[103],"preserving":[104],"binarization":[106],"enforcing":[107],"regularized":[108],"least-squares":[109],"functional.":[110],"parameters":[112],"defining":[113],"model":[116],"steps,":[120],"which,":[121],"unlike":[122],"most":[123],"deep":[124,178],"approaches,":[126],"all":[127],"have":[128],"precise":[130],"interpretable":[132],"meaning,":[133],"via":[136],"either":[137],"similarity":[139],"index":[140],"or":[141],"support":[143],"vector":[144],"strategy.":[146],"Numerical":[147],"experiments":[148],"on":[149],"test":[151],"images":[152],"provided":[153],"by":[154],"challenge":[156],"authors":[157],"show":[158],"significant":[159],"gains":[160],"respect":[162],"standard":[165],"performances":[169],"comparable":[170],"those":[172],"some":[174],"based":[180],"algorithms":[181],"require":[183],"millions":[187]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4307005500","counts_by_year":[],"updated_date":"2024-12-09T21:32:27.985982","created_date":"2022-10-22"}