{"id":"https://openalex.org/W4415708056","doi":"https://doi.org/10.1109/icme59968.2025.11209153","title":"ReF-LLE: Personalized Low-Light Enhancement via Reference-Guided Deep Reinforcement Learning","display_name":"ReF-LLE: Personalized Low-Light Enhancement via Reference-Guided Deep Reinforcement Learning","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415708056","doi":"https://doi.org/10.1109/icme59968.2025.11209153"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11209153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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/A5100846265","display_name":"Ming Zhao","orcid":"https://orcid.org/0000-0001-7431-1897"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Zhao","raw_affiliation_strings":["Jilin University,College of Software,Changchun,China,130012"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jilin University,College of Software,Changchun,China,130012","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100666029","display_name":"Pingping Liu","orcid":"https://orcid.org/0000-0002-0196-7913"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210134929","display_name":"Jilin Province Science and Technology Department","ror":"https://ror.org/049x38272","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pingping Liu","raw_affiliation_strings":["Jilin University,College of Computer Science and Technology,Changchun,China,130012"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jilin University,College of Computer Science and Technology,Changchun,China,130012","institution_ids":["https://openalex.org/I4210134929","https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004238321","display_name":"Tongshun Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210134929","display_name":"Jilin Province Science and Technology Department","ror":"https://ror.org/049x38272","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tongshun Zhang","raw_affiliation_strings":["Jilin University,College of Computer Science and Technology,Changchun,China,130012"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jilin University,College of Computer Science and Technology,Changchun,China,130012","institution_ids":["https://openalex.org/I4210134929","https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100442960","display_name":"Zhe Zhang","orcid":"https://orcid.org/0000-0001-9783-2521"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210134929","display_name":"Jilin Province Science and Technology Department","ror":"https://ror.org/049x38272","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Zhang","raw_affiliation_strings":["Jilin University,College of Computer Science and Technology,Changchun,China,130012"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jilin University,College of Computer Science and Technology,Changchun,China,130012","institution_ids":["https://openalex.org/I4210134929","https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9886000156402588,"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":0.9886000156402588,"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":0.001500000013038516,"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":0.0010999999940395355,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7573000192642212},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5425000190734863},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5090000033378601},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5002999901771545},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.4997999966144562},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44339999556541443},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38280001282691956},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.37369999289512634}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7573000192642212},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7470999956130981},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7276999950408936},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5425000190734863},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5090000033378601},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5002999901771545},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.4997999966144562},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46399998664855957},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44339999556541443},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38280001282691956},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.37369999289512634},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.37130001187324524},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.35589998960494995},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3248000144958496},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11209153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1580436348","https://openalex.org/W1987444808","https://openalex.org/W2054814429","https://openalex.org/W2102166818","https://openalex.org/W2133665775","https://openalex.org/W2566376500","https://openalex.org/W2962785568","https://openalex.org/W2988734173","https://openalex.org/W3003838261","https://openalex.org/W3013338555","https://openalex.org/W3030380536","https://openalex.org/W3035731588","https://openalex.org/W3125869362","https://openalex.org/W3199334903","https://openalex.org/W3206516528","https://openalex.org/W4205944311","https://openalex.org/W4225559324","https://openalex.org/W4310138363","https://openalex.org/W4312249431","https://openalex.org/W4312725970","https://openalex.org/W4312746248","https://openalex.org/W4385764562","https://openalex.org/W4386066362","https://openalex.org/W4387967967","https://openalex.org/W4390871817","https://openalex.org/W4390872514","https://openalex.org/W4390874206","https://openalex.org/W4402715957","https://openalex.org/W4402754029"],"related_works":[],"abstract_inverted_index":{"Low-light":[0],"image":[1,38,69,164],"enhancement":[2,39,142],"presents":[3],"two":[4],"primary":[5],"challenges:":[6],"1)":[7],"Significant":[8],"variations":[9],"in":[10,43,109,161],"low-light":[11,37,90,127,163],"images":[12,128],"across":[13],"different":[14],"conditions,":[15],"and":[16,24,48,159],"2)":[17],"Enhancement":[18],"levels":[19],"influenced":[20],"by":[21,105],"subjective":[22],"preferences":[23],"user":[25],"intent.":[26],"To":[27],"address":[28],"these":[29],"issues,":[30],"we":[31],"propose":[32],"ReF-LLE,":[33],"a":[34,67,99,136],"novel":[35],"personalized":[36,100,141,162],"method":[40],"that":[41,81,150],"operates":[42],"the":[44,55,83,94,106,110,115,122,132],"Fourier":[45,111],"frequency":[46],"domain":[47],"incorporates":[49],"deep":[50,59],"reinforcement":[51,60],"learning.":[52],"ReF-LLE":[53,97,151],"is":[54,72],"first":[56],"to":[57,74,85,124,129],"integrate":[58],"learning":[61],"into":[62],"this":[63],"domain.":[64],"During":[65],"training,":[66],"zero-reference":[68],"evaluation":[70],"strategy":[71,120],"introduced":[73],"score":[75],"enhanced":[76],"images,":[77],"providing":[78],"reward":[79],"signals":[80],"guide":[82],"model":[84,123],"handle":[86],"varying":[87],"degrees":[88],"of":[89,135],"conditions":[91],"effectively.":[92],"In":[93],"inference":[95],"phase,":[96],"employs":[98],"adaptive":[101],"iterative":[102],"strategy,":[103],"guided":[104],"zero-frequency":[107],"component":[108],"domain,":[112],"which":[113],"represents":[114],"overall":[116],"illumination":[117,133],"level.":[118],"This":[119],"enables":[121],"adaptively":[125],"adjust":[126],"align":[130],"with":[131],"distribution":[134],"user-provided":[137],"reference":[138],"image,":[139],"ensuring":[140],"results.":[143],"Extensive":[144],"experiments":[145],"on":[146],"benchmark":[147],"datasets":[148],"demonstrate":[149],"outperforms":[152],"state-of-the-art":[153],"methods,":[154],"achieving":[155],"superior":[156],"perceptual":[157],"quality":[158],"adaptability":[160],"enhancement.":[165]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-30T00:00:00"}