{"id":"https://openalex.org/W4214810407","doi":"https://doi.org/10.1109/tnnls.2022.3151099","title":"Blind Attention Geometric Restraint Neural Network for Single Image Dynamic/Defocus Deblurring","display_name":"Blind Attention Geometric Restraint Neural Network for Single Image Dynamic/Defocus Deblurring","publication_year":2022,"publication_date":"2022-03-02","ids":{"openalex":"https://openalex.org/W4214810407","doi":"https://doi.org/10.1109/tnnls.2022.3151099","pmid":"https://pubmed.ncbi.nlm.nih.gov/35235524"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2022.3151099","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3151099","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jie Zhang","orcid":"https://orcid.org/0000-0003-0901-5789"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jie Zhang","raw_affiliation_strings":["Department of Measuring and Controlling Technology, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-0901-5789","affiliations":[{"raw_affiliation_string":"Department of Measuring and Controlling Technology, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":null,"display_name":"Wanming Zhai","orcid":"https://orcid.org/0000-0002-9490-8217"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanming Zhai","raw_affiliation_strings":["State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-9490-8217","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4800084"],"apc_list":null,"apc_paid":null,"fwci":3.0476,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.92632136,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"34","issue":"11","first_page":"8404","last_page":"8417"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9765999913215637,"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":0.9765999913215637,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.007699999958276749,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.005100000184029341,"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/deblurring","display_name":"Deblurring","score":0.9611999988555908},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.7526000142097473},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5613999962806702},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5534999966621399},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5005999803543091},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4684000015258789},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44339999556541443}],"concepts":[{"id":"https://openalex.org/C2777693668","wikidata":"https://www.wikidata.org/wiki/Q25053743","display_name":"Deblurring","level":5,"score":0.9611999988555908},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7526000142097473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7294999957084656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7257000207901001},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5613999962806702},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5534999966621399},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5257999897003174},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5005999803543091},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4684000015258789},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44339999556541443},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.44020000100135803},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.29019999504089355},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.28029999136924744},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2022.3151099","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3151099","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:35235524","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35235524","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1470834973","display_name":null,"funder_award_id":"11790280","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5074055955","display_name":null,"funder_award_id":"51775449","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7243768455","display_name":null,"funder_award_id":"51205323","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1568804278","https://openalex.org/W1598281290","https://openalex.org/W1916731006","https://openalex.org/W1916935112","https://openalex.org/W1917431891","https://openalex.org/W1950693991","https://openalex.org/W2044005793","https://openalex.org/W2276154416","https://openalex.org/W2465552163","https://openalex.org/W2560533888","https://openalex.org/W2564023417","https://openalex.org/W2609107023","https://openalex.org/W2738579427","https://openalex.org/W2759428153","https://openalex.org/W2767829160","https://openalex.org/W2780624730","https://openalex.org/W2798735168","https://openalex.org/W2900162782","https://openalex.org/W2912512021","https://openalex.org/W2921478860","https://openalex.org/W2948208276","https://openalex.org/W2955103784","https://openalex.org/W2961218591","https://openalex.org/W2962793481","https://openalex.org/W2963130865","https://openalex.org/W2963312584","https://openalex.org/W2963667985","https://openalex.org/W2963800716","https://openalex.org/W2964030969","https://openalex.org/W2965217508","https://openalex.org/W2969717429","https://openalex.org/W2971688562","https://openalex.org/W2982795046","https://openalex.org/W3008427748","https://openalex.org/W3009001235","https://openalex.org/W3009562877","https://openalex.org/W3012451788","https://openalex.org/W3017317138","https://openalex.org/W3018074695","https://openalex.org/W3025353292","https://openalex.org/W3034347085","https://openalex.org/W3034724715","https://openalex.org/W3035086889","https://openalex.org/W3035484352","https://openalex.org/W3037948385","https://openalex.org/W3048364239","https://openalex.org/W3083529857","https://openalex.org/W3085615344","https://openalex.org/W3095435396","https://openalex.org/W3109494165","https://openalex.org/W3126501190","https://openalex.org/W3171125843","https://openalex.org/W3176880348","https://openalex.org/W4254508956","https://openalex.org/W6735913928","https://openalex.org/W6796090379","https://openalex.org/W6796298902"],"related_works":[],"abstract_inverted_index":{"Based":[0],"on":[1,63,79,105,140,154],"the":[2,7,20,27,32,40,45,57,80,87,93,99,109,114,118,134,147,151],"information":[3],"loss":[4],"analysis":[5],"of":[6,56,117],"blur":[8],"accumulation":[9],"model,":[10],"a":[11,72],"novel":[12],"single-image":[13],"deblurring":[14,41],"method":[15,136,149],"is":[16],"proposed.":[17],"We":[18],"apply":[19],"recurrent":[21],"neural":[22],"network":[23,35],"architecture":[24,37],"to":[25,38,49,60,91],"capture":[26],"attention":[28,46,75,127],"perception":[29],"map":[30],"and":[31,108,142,158],"generative":[33],"adversarial":[34],"(GAN)":[36],"yield":[39],"image.":[42],"Considering":[43],"that":[44,98,146],"mechanism":[47],"has":[48],"make":[50],"hard":[51],"decisions":[52],"about":[53],"specific":[54],"parts":[55],"input":[58],"image":[59],"be":[61],"focused":[62],"since":[64],"blurry":[65,106,160],"regions":[66],"are":[67],"not":[68],"given,":[69],"we":[70,122,132],"propose":[71],"new":[73],"adaptive":[74],"disentanglement":[76],"model":[77],"based":[78],"variation":[81],"blind":[82,124],"source":[83,125],"separation,":[84,126],"which":[85],"provides":[86],"global":[88],"geometric":[89,128],"restraint":[90,129],"reduce":[92],"large":[94],"solution":[95],"space,":[96],"so":[97],"generator":[100],"can":[101,111],"realistically":[102],"restore":[103],"details":[104],"regions,":[107],"discriminator":[110],"accurately":[112],"assess":[113],"content":[115],"consistency":[116],"restored":[119],"regions.":[120],"Since":[121],"combine":[123],"with":[130],"GANs,":[131],"name":[133],"proposed":[135,148],"BAGdeblur.":[137],"Extensive":[138],"evaluations":[139],"quantitative":[141],"qualitative":[143],"experiments":[144],"show":[145],"achieves":[150],"state-of-the-art":[152],"performance":[153],"both":[155],"synthetic":[156],"datasets":[157],"real-world":[159],"images.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":3}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2022-03-05T00:00:00"}
