{"id":"https://openalex.org/W7138290815","doi":"https://doi.org/10.48550/arxiv.2603.13363","title":"IAML: Illumination-Aware Mirror Loss for Progressive Learning in Low-Light Image Enhancement Auto-encoders","display_name":"IAML: Illumination-Aware Mirror Loss for Progressive Learning in Low-Light Image Enhancement Auto-encoders","publication_year":2026,"publication_date":"2026-03-09","ids":{"openalex":"https://openalex.org/W7138290815","doi":"https://doi.org/10.48550/arxiv.2603.13363"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.13363","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13363","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.13363","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Mohsen, Farida","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mohsen, Farida","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zaim, Tala","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zaim, Tala","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Al-Zawqari, Ali","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Al-Zawqari, Ali","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Safa, Ali","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Safa, Ali","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Belhaouari, Samir","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Belhaouari, Samir","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.8637999892234802,"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":0.8637999892234802,"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":0.04100000113248825,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.020800000056624413,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/feature","display_name":"Feature (linguistics)","score":0.7396000027656555},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.6349999904632568},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5070000290870667},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4180999994277954},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.388700008392334},{"id":"https://openalex.org/keywords/image-enhancement","display_name":"Image enhancement","score":0.3837999999523163},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.38100001215934753},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.36809998750686646}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7396000027656555},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6875},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6669999957084656},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6349999904632568},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.579200029373169},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5070000290870667},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4180999994277954},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.388700008392334},{"id":"https://openalex.org/C3017601658","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image enhancement","level":3,"score":0.3837999999523163},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.38100001215934753},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.32339999079704285},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.287200003862381},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C2987933465","wikidata":"https://www.wikidata.org/wiki/Q141130","display_name":"Image manipulation","level":3,"score":0.27219998836517334},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.13363","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13363","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.13363","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13363","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"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":{"This":[0],"letter":[1],"presents":[2],"a":[3,23,29,52,56],"novel":[4],"training":[5],"approach":[6,17,32,103],"and":[7,124],"loss":[8,58],"function":[9,59],"for":[10],"learning":[11,31],"low-light":[12,107],"image":[13,38,108,143],"enhancement":[14,109],"auto-encoders.":[15],"Our":[16],"revolves":[18],"around":[19],"the":[20,48,68,72,82,89,95,137,142],"use":[21],"of":[22,47,91,100,120,139],"teacher-student":[24],"auto-encoder":[25],"setup":[26],"coupled":[27],"to":[28,134],"progressive":[30],"where":[33],"multi-scale":[34],"information":[35],"from":[36,81],"clean":[37,77],"decoder":[39,50,74],"feature":[40,69,78],"maps":[41,70,79],"is":[42],"distilled":[43],"into":[44,87],"each":[45],"layer":[46],"student":[49,73],"in":[51,118],"mirrored":[53],"fashion":[54],"using":[55],"newly-proposed":[57],"termed":[60],"Illumination-Aware":[61],"Mirror":[62],"Loss":[63],"(IAML).":[64],"IAML":[65,140],"helps":[66],"aligning":[67],"within":[71,94],"network":[75],"with":[76],"originating":[80],"teacher":[83],"side":[84],"while":[85],"taking":[86],"account":[88],"effect":[90,138],"lighting":[92],"variations":[93],"input":[96],"images.":[97],"Extensive":[98],"benchmarking":[99],"our":[101,113],"proposed":[102],"on":[104,141],"three":[105],"popular":[106],"datasets":[110],"demonstrate":[111,136],"that":[112],"model":[114],"achieves":[115],"state-of-the-art":[116],"performance":[117],"terms":[119],"average":[121],"SSIM,":[122],"PSNR":[123],"LPIPS":[125],"reconstruction":[126,144],"accuracy":[127],"metrics.":[128],"Finally,":[129],"ablation":[130],"studies":[131],"are":[132],"performed":[133],"clearly":[135],"accuracy.":[145]},"counts_by_year":[],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2026-03-12T00:00:00"}
