{"id":"https://openalex.org/W4387967930","doi":"https://doi.org/10.1145/3581783.3612462","title":"Event-Diffusion: Event-Based Image Reconstruction and Restoration with Diffusion Models","display_name":"Event-Diffusion: Event-Based Image Reconstruction and Restoration with Diffusion Models","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4387967930","doi":"https://doi.org/10.1145/3581783.3612462"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3612462","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612462","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","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/A5014755327","display_name":"Quanmin Liang","orcid":"https://orcid.org/0000-0001-9935-5167"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quanmin Liang","raw_affiliation_strings":["Sun Yat-Sen University &amp; Peng Cheng Laboratory, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9935-5167","affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University &amp; Peng Cheng Laboratory, Guangzhou, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054226277","display_name":"Xiawu Zheng","orcid":"https://orcid.org/0000-0002-6855-5403"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiawu Zheng","raw_affiliation_strings":["Peng Cheng Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-6855-5403","affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100768452","display_name":"Kai Huang","orcid":"https://orcid.org/0000-0003-0359-7810"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Huang","raw_affiliation_strings":["Sun Yat-Sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0359-7810","affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030121180","display_name":"Yan Zhang","orcid":"https://orcid.org/0000-0003-1642-0758"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Zhang","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0003-1642-0758","affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100332984","display_name":"Jie Chen","orcid":"https://orcid.org/0000-0002-9765-4523"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Chen","raw_affiliation_strings":["Peng Cheng Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-9765-4523","affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023918894","display_name":"Yonghong Tian","orcid":"https://orcid.org/0000-0002-2978-5935"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghong Tian","raw_affiliation_strings":["Peking University &amp; Peng Cheng Laboratory, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2978-5935","affiliations":[{"raw_affiliation_string":"Peking University &amp; Peng Cheng Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3837","last_page":"3846"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11996","display_name":"Random lasers and scattering media","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/3102","display_name":"Acoustics and Ultrasonics"},"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9789999723434448,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7178124785423279},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7025043368339539},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6749608516693115},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5407206416130066},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.5275920033454895},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5060338377952576},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.48146048188209534},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.44451743364334106},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.43733111023902893},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4360593557357788},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.41717758774757385},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39240869879722595},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.29316964745521545},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.25440168380737305}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7178124785423279},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7025043368339539},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6749608516693115},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5407206416130066},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.5275920033454895},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5060338377952576},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.48146048188209534},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.44451743364334106},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.43733111023902893},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4360593557357788},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.41717758774757385},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39240869879722595},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.29316964745521545},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25440168380737305},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3612462","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612462","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W206948248","https://openalex.org/W1861492603","https://openalex.org/W2133665775","https://openalex.org/W2472115300","https://openalex.org/W2519559998","https://openalex.org/W2565755350","https://openalex.org/W2567239141","https://openalex.org/W2605111497","https://openalex.org/W2785582094","https://openalex.org/W2789435382","https://openalex.org/W2796368857","https://openalex.org/W2884195444","https://openalex.org/W2886851211","https://openalex.org/W2901157565","https://openalex.org/W2921116036","https://openalex.org/W2962785568","https://openalex.org/W2964132169","https://openalex.org/W2964160927","https://openalex.org/W2964615694","https://openalex.org/W2964981172","https://openalex.org/W2965775211","https://openalex.org/W2966103660","https://openalex.org/W2998281665","https://openalex.org/W3010556452","https://openalex.org/W3034419760","https://openalex.org/W3034685081","https://openalex.org/W3035460915","https://openalex.org/W3040838455","https://openalex.org/W3091146474","https://openalex.org/W3096831136","https://openalex.org/W3097980825","https://openalex.org/W3101118898","https://openalex.org/W3105213754","https://openalex.org/W3109192943","https://openalex.org/W3110052477","https://openalex.org/W3122190729","https://openalex.org/W3155072588","https://openalex.org/W3174491972","https://openalex.org/W3205269907","https://openalex.org/W3212516020","https://openalex.org/W4213073032","https://openalex.org/W4214514519","https://openalex.org/W4214660013","https://openalex.org/W4252813286","https://openalex.org/W4312293341","https://openalex.org/W4312497550","https://openalex.org/W4312570054","https://openalex.org/W4312740349","https://openalex.org/W4312933868","https://openalex.org/W7139033639"],"related_works":["https://openalex.org/W2494523064","https://openalex.org/W2943623134","https://openalex.org/W2588219639","https://openalex.org/W2215759665","https://openalex.org/W2030292806","https://openalex.org/W2960358116","https://openalex.org/W4287727129","https://openalex.org/W3041172967","https://openalex.org/W2749065928","https://openalex.org/W2147155098"],"abstract_inverted_index":{"Event":[0],"cameras":[1],"offer":[2],"the":[3,19,34,62,71,78,81,91,96,102,109,133,156,160,167,175,186,201,206,211,224,229,234],"advantages":[4],"of":[5,24,36,74,90,106,112,203,242],"low":[6],"latency,":[7],"high":[8],"temporal":[9],"resolution":[10],"and":[11,21,47,53,76,104,122,144,178,233,245,253],"HDR":[12],"compared":[13,248],"to":[14,18,51,64,69,136,171,192,199,249],"conventional":[15],"cameras.":[16],"Due":[17],"asynchronous":[20],"sparse":[22],"nature":[23],"events,":[25],"many":[26],"existing":[27,40],"algorithms":[28],"cannot":[29],"be":[30],"directly":[31],"applied,":[32],"necessitating":[33],"reconstruction":[35,41,67,134],"intensity":[37],"frames.":[38],"However,":[39],"methods":[42],"often":[43],"result":[44],"in":[45,80,95,118,146,205,223],"artifacts":[46,79,143],"edge":[48,72,86,153,182,187],"blurring":[49],"due":[50],"noise":[52,173],"event":[54,97,157,176],"accumulation.":[55],"In":[56],"this":[57],"paper,":[58],"we":[59,124,150],"argue":[60],"that":[61,217],"key":[63],"event-based":[65,162],"image":[66,119],"is":[68,88,189,197],"enhance":[70,200],"information":[73,87,100,154,188],"objects":[75,204],"restore":[77],"reconstructed":[82,147,207],"images.":[83,148],"To":[84],"explain,":[85],"one":[89],"most":[92],"important":[93],"features":[94],"stream,":[98],"providing":[99],"on":[101],"shape":[103],"contour":[105],"objects.":[107],"Considering":[108],"extraordinary":[110],"capabilities":[111],"Denoising":[113],"Diffusion":[114],"Probabilistic":[115],"Models":[116],"(DDPMs)":[117],"generation,":[120],"reconstruction,":[121],"restoration,":[123],"propose":[125],"a":[126],"new":[127],"framework":[128,170],"which":[129,140,196],"incorporate":[130],"it":[131],"into":[132],"pipeline":[135],"obtain":[137],"high-quality":[138],"results":[139,215],"effectively":[141],"remove":[142,172],"blur":[145],"Specifically,":[149],"first":[151],"extract":[152,179],"from":[155,174],"stream":[158,177],"using":[159],"proposed":[161],"denoising":[163],"method.":[164],"It":[165],"employs":[166],"contrast":[168],"maximization":[169],"clear":[180],"object":[181],"information.":[183],"And":[184],"then,":[185],"further":[190],"adopted":[191],"our":[193,218],"diffusion":[194],"model,":[195],"used":[198],"edges":[202],"images,":[208],"thus":[209],"improving":[210],"restoration":[212],"effect.":[213],"Experimental":[214],"show":[216],"method":[219],"achieves":[220],"significant":[221],"improvements":[222,241],"mean":[225],"squared":[226],"error":[227],"(MSE),":[228],"structural":[230],"similarity":[231,236],"(SSIM),":[232],"perceptual":[235],"(LPIPS)":[237],"metrics,":[238],"with":[239],"average":[240],"40%,":[243],"15%,":[244],"25%,":[246],"respectively,":[247],"previous":[250],"state-of-the-art":[251],"models,":[252],"has":[254],"good":[255],"generalization":[256],"performance.":[257]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
