{"id":"https://openalex.org/W7101725152","doi":"https://doi.org/10.48550/arxiv.2510.21815","title":"HDR Image Reconstruction using an Unsupervised Fusion Model","display_name":"HDR Image Reconstruction using an Unsupervised Fusion Model","publication_year":2025,"publication_date":"2025-10-21","ids":{"openalex":"https://openalex.org/W7101725152","doi":"https://doi.org/10.48550/arxiv.2510.21815"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2510.21815","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.21815","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,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2510.21815","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Nagaswetha, Kumbha","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nagaswetha, Kumbha","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":false,"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":true,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9514999985694885,"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":0.9514999985694885,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.03819999843835831,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.0024999999441206455,"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/fuse","display_name":"Fuse (electrical)","score":0.6534000039100647},{"id":"https://openalex.org/keywords/high-dynamic-range","display_name":"High dynamic range","score":0.6498000025749207},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.51910001039505},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.4392000138759613},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4291999936103821},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4203000068664551},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3903999924659729},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3837999999523163},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.3720000088214874}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8082000017166138},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7918999791145325},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.65420001745224},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.6534000039100647},{"id":"https://openalex.org/C2780056265","wikidata":"https://www.wikidata.org/wiki/Q106239881","display_name":"High dynamic range","level":3,"score":0.6498000025749207},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.51910001039505},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.4392000138759613},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4291999936103821},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4203000068664551},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3903999924659729},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3837999999523163},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3720000088214874},{"id":"https://openalex.org/C125245961","wikidata":"https://www.wikidata.org/wiki/Q221656","display_name":"Brightness","level":2,"score":0.3506999909877777},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3411000072956085},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.33149999380111694},{"id":"https://openalex.org/C2781399445","wikidata":"https://www.wikidata.org/wiki/Q309254","display_name":"High-dynamic-range imaging","level":4,"score":0.32030001282691956},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32030001282691956},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.30970001220703125},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.30709999799728394},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.29339998960494995},{"id":"https://openalex.org/C87133666","wikidata":"https://www.wikidata.org/wiki/Q1161699","display_name":"Dynamic range","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2734000086784363},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.26649999618530273}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2510.21815","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.21815","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,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2510.21815","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.21815","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,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"High":[0],"Dynamic":[1,64],"Range":[2,65],"(HDR)":[3],"imaging":[4],"aims":[5],"to":[6,31,34,77,110,164,175],"reproduce":[7],"the":[8,19,97,105,146],"wide":[9],"range":[10],"of":[11,60],"brightness":[12],"levels":[13],"present":[14],"in":[15,93,102,120],"natural":[16],"scenes,":[17],"which":[18],"human":[20],"visual":[21,161],"system":[22],"can":[23],"perceive":[24],"but":[25],"conventional":[26],"digital":[27],"cameras":[28],"often":[29],"fail":[30],"capture":[32],"due":[33],"their":[35,79],"limited":[36],"dynamic":[37],"range.":[38],"To":[39],"address":[40],"this":[41],"limitation,":[42],"we":[43],"propose":[44],"a":[45,58,83,112],"deep":[46],"learning-based":[47],"multi-exposure":[48],"fusion":[49,166],"approach":[50,158],"for":[51,133],"HDR":[52,114,128],"image":[53,90,99],"generation.":[54],"The":[55,88,116],"method":[56],"takes":[57],"set":[59],"differently":[61],"exposed":[62],"Low":[63],"(LDR)":[66],"images,":[67,129],"typically":[68],"an":[69,72,121],"underexposed":[70,89],"and":[71,75,154,179],"overexposed":[73,98],"image,":[74],"learns":[76],"fuse":[78],"complementary":[80],"information":[81,101],"using":[82,145],"convolutional":[84],"neural":[85],"network":[86,106],"(CNN).":[87],"preserves":[91],"details":[92],"bright":[94],"regions,":[95],"while":[96],"retains":[100],"dark":[103],"regions;":[104],"effectively":[107],"combines":[108],"these":[109],"reconstruct":[111],"high-quality":[113],"output.":[115],"model":[117,181],"is":[118,139,172],"trained":[119],"unsupervised":[122],"manner,":[123],"without":[124],"relying":[125],"on":[126],"ground-truth":[127],"making":[130],"it":[131],"practical":[132],"real-world":[134],"applications":[135],"where":[136],"such":[137],"data":[138],"unavailable.":[140],"We":[141],"evaluate":[142],"our":[143,157],"results":[144],"Multi-Exposure":[147],"Fusion":[148],"Structural":[149],"Similarity":[150],"Index":[151],"Measure":[152],"(MEF-SSIM)":[153],"demonstrate":[155],"that":[156],"achieves":[159],"superior":[160],"quality":[162],"compared":[163],"existing":[165],"methods.":[167],"A":[168],"customized":[169],"loss":[170],"function":[171],"further":[173],"introduced":[174],"improve":[176],"reconstruction":[177],"fidelity":[178],"optimize":[180],"performance.":[182]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-29T00:00:00"}
