{"id":"https://openalex.org/W7224079482","doi":"https://doi.org/10.48550/arxiv.2610.37135","title":"Physics Guided Diffusion Model Based Exposure Interpolation for High Dynamic Range Imaging","display_name":"Physics Guided Diffusion Model Based Exposure Interpolation for High Dynamic Range Imaging","publication_year":2026,"publication_date":"2026-10-27","ids":{"openalex":"https://openalex.org/W7224079482","doi":"https://doi.org/10.48550/arxiv.2610.37135"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.37135","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.37135","pdf_url":"https://arxiv.org/pdf/2610.37135","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.37135","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5159988525","display_name":"Zhengguo Li","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhengguo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5005367753","display_name":"Chaobing Zheng","orcid":"https://orcid.org/0000-0002-4305-8697","observed_orcids":["https://orcid.org/0000-0002-4305-8697"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Chaobing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9937000274658203,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9937000274658203,"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/T11105","display_name":"Advanced Image Processing Techniques","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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.00019999999494757503,"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/high-dynamic-range-imaging","display_name":"high dynamic range imaging","score":0.9860000014305115},{"id":"https://openalex.org/keywords/ghosting-artifacts","display_name":"ghosting artifacts","score":0.7879999876022339},{"id":"https://openalex.org/keywords/pearson-correlation","display_name":"Pearson correlation","score":0.48399999737739563},{"id":"https://openalex.org/keywords/high-frequency-detail-preservation","display_name":"high-frequency detail preservation","score":0.5220000147819519}],"concepts":[{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.8065999746322632},{"id":"https://openalex.org/C2780531524","wikidata":"https://www.wikidata.org/wiki/Q551540","display_name":"Ghosting","level":2,"score":0.7506999969482422},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5454000234603882},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5288000106811523},{"id":"https://openalex.org/C2780056265","wikidata":"https://www.wikidata.org/wiki/Q106239881","display_name":"High dynamic range","level":3,"score":0.5138000249862671},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5008999705314636},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47040000557899475},{"id":"https://openalex.org/C87133666","wikidata":"https://www.wikidata.org/wiki/Q1161699","display_name":"Dynamic range","level":2,"score":0.4648999869823456},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.4618000090122223},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.45649999380111694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4447000026702881},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41769999265670776},{"id":"https://openalex.org/C27624317","wikidata":"https://www.wikidata.org/wiki/Q263499","display_name":"Demosaicing","level":5,"score":0.39649999141693115},{"id":"https://openalex.org/C203504353","wikidata":"https://www.wikidata.org/wiki/Q4765461","display_name":"Anisotropic diffusion","level":3,"score":0.3880999982357025},{"id":"https://openalex.org/C27405340","wikidata":"https://www.wikidata.org/wiki/Q440296","display_name":"Image scaling","level":4,"score":0.3862999975681305},{"id":"https://openalex.org/C119768884","wikidata":"https://www.wikidata.org/wiki/Q7597108","display_name":"Stairstep interpolation","level":4,"score":0.35580000281333923},{"id":"https://openalex.org/C125245961","wikidata":"https://www.wikidata.org/wiki/Q221656","display_name":"Brightness","level":2,"score":0.3553999960422516},{"id":"https://openalex.org/C203332170","wikidata":"https://www.wikidata.org/wiki/Q6334079","display_name":"Multivariate interpolation","level":3,"score":0.35350000858306885},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33550000190734863},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.33180001378059387},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C2781399445","wikidata":"https://www.wikidata.org/wiki/Q309254","display_name":"High-dynamic-range imaging","level":4,"score":0.31769999861717224},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C171836373","wikidata":"https://www.wikidata.org/wiki/Q2266329","display_name":"Linear interpolation","level":3,"score":0.310699999332428},{"id":"https://openalex.org/C207214200","wikidata":"https://www.wikidata.org/wiki/Q4202129","display_name":"Nearest-neighbor interpolation","level":4,"score":0.3073999881744385},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.30550000071525574},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.2842000126838684},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.26930001378059387},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.25850000977516174},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.25440001487731934}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.37135","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.37135","pdf_url":"https://arxiv.org/pdf/2610.37135","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.37135","doi":"https://doi.org/10.48550/arxiv.2610.37135","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.37135","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.37135","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.37135","pdf_url":"https://arxiv.org/pdf/2610.37135","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7224079482.pdf","grobid_xml":"https://content.openalex.org/works/W7224079482.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7203570115","https://openalex.org/W4409077074","https://openalex.org/W7163808835","https://openalex.org/W4386590406","https://openalex.org/W4224263501","https://openalex.org/W7171441870","https://openalex.org/W1972145754","https://openalex.org/W4394602533","https://openalex.org/W2467386848","https://openalex.org/W3173941784"],"abstract_inverted_index":{"Ghosting":[0],"artifacts":[1,37],"are":[2,38,45,102],"an":[3,54,57,175],"open":[4],"problem":[5],"for":[6],"popular":[7],"multi-shot":[8],"based":[9,77,109,116],"high":[10,181],"dynamic":[11,25],"range":[12,26],"(HDR)":[13],"imaging.":[14],"An":[15,88],"effective":[16],"solution":[17],"is":[18,50,81,91,115],"to":[19,52,83,130,178],"capture":[20],"two":[21,42,66],"large-exposure-ratio":[22],"(LER)":[23],"standard":[24],"(SDR)":[27],"images":[28,44],"simultaneously":[29],"via":[30],"a":[31,72,94,105,126],"single-shot.":[32],"Brightness":[33],"order":[34],"reversal":[35],"(BOR)":[36],"generated":[39,103],"when":[40],"the":[41,62,85,98,118,132,135,139,143,148,152,169,187],"LER":[43,67],"directly":[46],"fused":[47],"together.":[48],"It":[49],"crucial":[51],"interpolate":[53],"image":[55,90],"with":[56,156],"exposure":[58,63,78,160,171,189],"time":[59],"in":[60],"between":[61,138],"times":[64],"of":[65,121,134,151,165],"images.":[68],"In":[69],"this":[70],"paper,":[71],"novel":[73],"physics-guided":[74],"diffusion":[75,107,113],"model":[76,108,114],"interpolation":[79,161,172,190],"algorithm":[80,173],"proposed":[82,170,188],"address":[84],"BOR":[86],"artifacts.":[87],"initial":[89],"interpolated":[92],"by":[93,104],"physics-driven":[95],"component,":[96],"and":[97,124,142],"residual":[99],"high-frequency":[100],"details":[101],"new":[106,112,127],"component.":[110],"The":[111],"on":[117,163],"correlation":[119,137],"analysis":[120],"generative":[122],"models":[123],"includes":[125],"predicted":[128],"target":[129],"reduce":[131],"variation":[133],"Pearson":[136],"noisy":[140],"data":[141],"target,":[144],"as":[145,147],"well":[146],"amplification":[149],"factor":[150],"fitting":[153],"error.":[154],"Compared":[155],"existing":[157],"neural":[158,167],"augmented":[159],"algorithms":[162],"top":[164],"convolution":[166],"networks,":[168],"has":[174],"inherent":[176],"advantage":[177],"generate":[179],"more":[180],"frequency":[182],"information.":[183],"Experiment":[184],"results":[185],"validate":[186],"algorithm.":[191]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-29T00:00:00"}
