{"id":"https://openalex.org/W1910788271","doi":"https://doi.org/10.1109/cvpr.2015.7298777","title":"Blind optical aberration correction by exploring geometric and visual priors","display_name":"Blind optical aberration correction by exploring geometric and visual priors","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1910788271","doi":"https://doi.org/10.1109/cvpr.2015.7298777","mag":"1910788271"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298777","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101747516","display_name":"Tao Yue","orcid":"https://orcid.org/0000-0002-2952-8971"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tao Yue","raw_affiliation_strings":["Department of Automation, Tsinghua University","Department of Automation, Tsinghua University, Beijing (China)"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing (China)","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051445938","display_name":"Jinli Suo","orcid":"https://orcid.org/0000-0002-3426-1634"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinli Suo","raw_affiliation_strings":["Department of Automation, Tsinghua University","Department of Automation, Tsinghua University, Beijing (China)"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing (China)","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100440604","display_name":"Jue Wang","orcid":"https://orcid.org/0000-0002-3641-3136"},"institutions":[{"id":"https://openalex.org/I4210101198","display_name":"Adobe Gastroenterology","ror":"https://ror.org/015n4kv50","country_code":"US","type":"other","lineage":["https://openalex.org/I4210101198"]},{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jue Wang","raw_affiliation_strings":["Adobe Research","Adobe Research, Tucson, AZ 85712, United States"],"affiliations":[{"raw_affiliation_string":"Adobe Research","institution_ids":["https://openalex.org/I1306409833"]},{"raw_affiliation_string":"Adobe Research, Tucson, AZ 85712, United States","institution_ids":["https://openalex.org/I4210101198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058572381","display_name":"Xun Cao","orcid":"https://orcid.org/0000-0003-3094-4371"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xun Cao","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University","School of Electronic Science and Engineering, Nanjing University, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University","institution_ids":["https://openalex.org/I881766915"]},{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080722708","display_name":"Qionghai Dai","orcid":"https://orcid.org/0000-0001-7043-3061"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qionghai Dai","raw_affiliation_strings":["Department of Automation, Tsinghua University","Department of Automation, Tsinghua University, Beijing (China)"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing (China)","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101747516"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.3103,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.86390727,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1684","last_page":"1692"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9993000030517578,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9993000030517578,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9980000257492065,"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/image-warping","display_name":"Image warping","score":0.8135050535202026},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.7763048410415649},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7030758261680603},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.691238522529602},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6538769006729126},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6154099702835083},{"id":"https://openalex.org/keywords/reflection-symmetry","display_name":"Reflection symmetry","score":0.41782742738723755},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3314233422279358},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.18377435207366943}],"concepts":[{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.8135050535202026},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7763048410415649},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7030758261680603},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.691238522529602},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6538769006729126},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6154099702835083},{"id":"https://openalex.org/C133978748","wikidata":"https://www.wikidata.org/wiki/Q15955882","display_name":"Reflection symmetry","level":2,"score":0.41782742738723755},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3314233422279358},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.18377435207366943},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2015.7298777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298777","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.710.8875","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.710.8875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.juew.org/publication/cvpr15-opticalabberation.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W194222370","https://openalex.org/W1550634749","https://openalex.org/W1965300224","https://openalex.org/W1976730913","https://openalex.org/W1978333359","https://openalex.org/W1987075379","https://openalex.org/W1992060453","https://openalex.org/W1996208406","https://openalex.org/W2017252663","https://openalex.org/W2029353064","https://openalex.org/W2031103533","https://openalex.org/W2061645611","https://openalex.org/W2067861369","https://openalex.org/W2074502458","https://openalex.org/W2080322915","https://openalex.org/W2080592425","https://openalex.org/W2094459065","https://openalex.org/W2097856232","https://openalex.org/W2111098473","https://openalex.org/W2135238609","https://openalex.org/W2147298660","https://openalex.org/W3098843502","https://openalex.org/W4232454059","https://openalex.org/W6633232966","https://openalex.org/W6665999919","https://openalex.org/W6676365840","https://openalex.org/W6681807782"],"related_works":["https://openalex.org/W1670332068","https://openalex.org/W2095618524","https://openalex.org/W2735770592","https://openalex.org/W1971024059","https://openalex.org/W1502062143","https://openalex.org/W4224236531","https://openalex.org/W4291993329","https://openalex.org/W2063177452","https://openalex.org/W2413982977","https://openalex.org/W2268850994"],"abstract_inverted_index":{"Optical":[0],"aberration":[1,29,95],"widely":[2],"exists":[3],"in":[4,9,82,120],"optical":[5],"imaging":[6],"systems,":[7],"especially":[8],"consumer-level":[10,100],"cameras.":[11],"In":[12],"contrast":[13],"to":[14,48,124,136],"previous":[15],"solutions":[16],"using":[17],"hardware":[18],"compensation":[19],"or":[20],"pre-calibration,":[21],"we":[22],"propose":[23],"a":[24,32,121],"computational":[25],"approach":[26],"for":[27],"blind":[28],"removal":[30],"from":[31,128],"single":[33],"image,":[34],"by":[35,57,89,99],"exploring":[36],"various":[37],"geometric":[38,80],"and":[39,62,74,84,110,132,141,148],"visual":[40,92,117],"priors.":[41],"The":[42],"global":[43],"rotational":[44],"symmetry":[45,69,73,76],"allows":[46],"us":[47],"transform":[49],"the":[50,58,91,102,111,126,129,138,153,157],"non-uniform":[51,103],"degeneration":[52],"into":[53],"several":[54],"uniform":[55],"ones":[56],"proposed":[59,154],"radial":[60],"splitting":[61],"warping":[63],"technique.":[64],"Locally,":[65],"two":[66],"types":[67],"of":[68,94,105],"constraints,":[70],"i.e.":[71],"central":[72,83],"reflection":[75],"are":[77],"defined":[78],"as":[79,116],"priors":[81],"surrounding":[85],"regions,":[86],"respectively.":[87],"Furthermore,":[88],"investigating":[90],"artifacts":[93],"degenerated":[96],"images":[97],"captured":[98],"cameras,":[101],"distribution":[104],"sharpness":[106],"across":[107],"color":[108],"channels":[109],"image":[112,134],"lattice":[113],"is":[114],"exploited":[115],"priors,":[118],"resulting":[119],"novel":[122],"strategy":[123],"utilize":[125],"guidance":[127],"sharpest":[130],"channel":[131],"local":[133],"regions":[135],"improve":[137],"overall":[139],"performance":[140],"robustness.":[142],"Extensive":[143],"evaluation":[144],"on":[145],"both":[146],"real":[147],"synthetic":[149],"data":[150],"suggests":[151],"that":[152],"method":[155],"outperforms":[156],"state-of-the-art":[158],"techniques.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
