{"id":"https://openalex.org/W4410887373","doi":"https://doi.org/10.1109/syscon64521.2025.11014822","title":"Lightweight Low-Light Image Enhancement Model Training and Design Considerations","display_name":"Lightweight Low-Light Image Enhancement Model Training and Design Considerations","publication_year":2025,"publication_date":"2025-04-07","ids":{"openalex":"https://openalex.org/W4410887373","doi":"https://doi.org/10.1109/syscon64521.2025.11014822"},"language":"en","primary_location":{"id":"doi:10.1109/syscon64521.2025.11014822","is_oa":false,"landing_page_url":"https://doi.org/10.1109/syscon64521.2025.11014822","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International systems Conference (SysCon)","raw_type":"proceedings-article"},"type":"article","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/A5045045011","display_name":"Hajira Saleem","orcid":"https://orcid.org/0000-0002-9596-2688"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hajira Saleem","raw_affiliation_strings":["Malm&#x00F6; University,Dept. of computer science,Malm&#x00F6;,Sweden,20506"],"affiliations":[{"raw_affiliation_string":"Malm&#x00F6; University,Dept. of computer science,Malm&#x00F6;,Sweden,20506","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001248508","display_name":"Reza Malekian","orcid":"https://orcid.org/0000-0002-2763-8085"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reza Malekian","raw_affiliation_strings":["Malm&#x00F6; University,Dept. of computer science,Malm&#x00F6;,Sweden,20506"],"affiliations":[{"raw_affiliation_string":"Malm&#x00F6; University,Dept. of computer science,Malm&#x00F6;,Sweden,20506","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032522974","display_name":"Hussan Munir","orcid":"https://orcid.org/0000-0001-9376-9844"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hussan Munir","raw_affiliation_strings":["Malm&#x00F6; University,Dept. of computer science,Malm&#x00F6;,Sweden,20506"],"affiliations":[{"raw_affiliation_string":"Malm&#x00F6; University,Dept. of computer science,Malm&#x00F6;,Sweden,20506","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5045045011"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.6381,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.90011334,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9761000275611877,"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":0.9761000275611877,"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/training","display_name":"Training (meteorology)","score":0.6872631907463074},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6573272943496704},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5138326287269592},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.45618027448654175},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43996986746788025},{"id":"https://openalex.org/keywords/image-enhancement","display_name":"Image enhancement","score":0.427217960357666},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3291977643966675}],"concepts":[{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6872631907463074},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6573272943496704},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5138326287269592},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45618027448654175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43996986746788025},{"id":"https://openalex.org/C3017601658","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image enhancement","level":3,"score":0.427217960357666},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3291977643966675},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/syscon64521.2025.11014822","is_oa":false,"landing_page_url":"https://doi.org/10.1109/syscon64521.2025.11014822","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International systems Conference (SysCon)","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":36,"referenced_works":["https://openalex.org/W1987444808","https://openalex.org/W2054814429","https://openalex.org/W2133665775","https://openalex.org/W2150066425","https://openalex.org/W2172275395","https://openalex.org/W2412926690","https://openalex.org/W2566376500","https://openalex.org/W2799265886","https://openalex.org/W2950689937","https://openalex.org/W2963349467","https://openalex.org/W2995569955","https://openalex.org/W3034347506","https://openalex.org/W3061447985","https://openalex.org/W3119525307","https://openalex.org/W3120540810","https://openalex.org/W3121661546","https://openalex.org/W3171708594","https://openalex.org/W3171760114","https://openalex.org/W3177656331","https://openalex.org/W4224130636","https://openalex.org/W4290713858","https://openalex.org/W4309740957","https://openalex.org/W4312678820","https://openalex.org/W4376464574","https://openalex.org/W4385636997","https://openalex.org/W4388348120","https://openalex.org/W4391676180","https://openalex.org/W4396566839","https://openalex.org/W4399435697","https://openalex.org/W4399729061","https://openalex.org/W4399885671","https://openalex.org/W4400490124","https://openalex.org/W4400491198","https://openalex.org/W4404305825","https://openalex.org/W6675164516","https://openalex.org/W6852638483"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W2949816130","https://openalex.org/W2138806349","https://openalex.org/W4362679294","https://openalex.org/W4375869153"],"abstract_inverted_index":{"Low-light":[0],"conditions":[1],"significantly":[2],"degrade":[3],"the":[4,75,140,204],"performance":[5,209],"of":[6,77],"RGB":[7],"cameras,":[8],"particularly":[9],"in":[10,37,128,227],"applications":[11],"like":[12],"visual":[13],"odometry":[14,81],"estimation":[15],"for":[16,24,54,80,93,181,240],"autonomous":[17,248],"vehicles,":[18],"where":[19,197],"feature":[20,129,212],"visibility":[21,76,130],"is":[22,178,206],"critical":[23],"accurate":[25],"navigation.":[26],"Traditional":[27],"image":[28,62,143,154,176,229,242],"enhancement":[29,63,96,155,243],"techniques":[30],"often":[31],"require":[32],"manual":[33],"tuning":[34],"and":[35,90,109,117,152,210,223,244,255],"struggle":[36],"extremely":[38],"dark":[39],"environments.":[40],"However,":[41],"deep-learning-based":[42],"methods,":[43],"though":[44,157],"promising,":[45],"typically":[46],"demand":[47],"high":[48],"computational":[49,85,253],"resources,":[50],"making":[51],"them":[52],"unsuitable":[53],"real-time":[55,94,133,182,208],"applications.":[56,183],"This":[57],"paper":[58],"presents":[59],"top":[60],"low-light":[61,95],"(LLIE)":[64],"models":[65,72,105,124],"obtained":[66],"through":[67],"our":[68,123,172,194,238],"experimentation":[69,113],"with":[70,106,114],"designing":[71],"to":[73,167,251],"improve":[74],"features":[78],"crucial":[79],"tasks":[82],"while":[83],"minimizing":[84],"overhead.":[86],"We":[87,135],"explore":[88],"design":[89],"training":[91,139,158],"strategies":[92],"using":[97,218],"U-Net,":[98],"a":[99,168],"CNN":[100,104],"architecture,":[101],"alongside":[102],"other":[103],"residual":[107,221],"blocks":[108,222],"attention":[110,224],"mechanisms.":[111],"Through":[112],"two":[115],"datasets-LOL-v2":[116],"KITTI":[118],"datasets,":[119],"we":[120,215],"demonstrate":[121],"that":[122,138,217],"offer":[125],"significant":[126],"improvements":[127],"without":[131,220],"compromising":[132],"performance.":[134,257],"also":[136],"report":[137],"model":[141,239],"on":[142,159,207,235],"patches":[144],"reduces":[145],"Graphics":[146],"processing":[147],"unit":[148],"(GPU)":[149],"memory":[150],"usage":[151],"improves":[153],"quality,":[156],"full":[160],"images":[161],"may":[162],"sometimes":[163],"be":[164],"necessary.":[165],"Compared":[166],"high-performing":[169],"baseline":[170],"model,":[171,196],"approach-despite":[173],"yielding":[174],"lower":[175],"quality":[177],"better":[179],"suited":[180],"Some":[184],"use":[185],"cases,":[186],"such":[187],"as":[188,203],"robotic":[189],"navigation,":[190],"can":[191],"benefit":[192],"from":[193],"lightweight":[195],"high-resolution":[198],"details":[199],"are":[200],"less":[201],"critical,":[202],"focus":[205,234],"general":[211],"visibility.":[213],"Additionally,":[214],"find":[216],"U-Net":[219],"mechanisms":[225],"results":[226],"degraded":[228],"quality.":[230],"Future":[231],"work":[232],"will":[233],"transfer":[236],"learning":[237],"odometryspecific":[241],"integrating":[245],"it":[246],"into":[247],"localization":[249],"systems":[250],"optimize":[252],"efficiency":[254],"enhance":[256]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
