{"id":"https://openalex.org/W7123492776","doi":"https://doi.org/10.1109/lsp.2026.3653616","title":"FFE-DETR: Frequency-Aware Feature Enhancement for Object Detection in Low-Light Scenarios","display_name":"FFE-DETR: Frequency-Aware Feature Enhancement for Object Detection in Low-Light Scenarios","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7123492776","doi":"https://doi.org/10.1109/lsp.2026.3653616"},"language":null,"primary_location":{"id":"doi:10.1109/lsp.2026.3653616","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2026.3653616","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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/A5023169843","display_name":"Y Li","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yufeng Li","raw_affiliation_strings":["College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063567039","display_name":"Chao Song","orcid":"https://orcid.org/0000-0001-5590-8663"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Song","raw_affiliation_strings":["College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082500328","display_name":"Chuanlong Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuanlong Xie","raw_affiliation_strings":["College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5023169843"],"corresponding_institution_ids":["https://openalex.org/I125904092"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09639152,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"678","last_page":"682"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.694100022315979,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.694100022315979,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.15129999816417694,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.02419999986886978,"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/object-detection","display_name":"Object detection","score":0.7623999714851379},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6467999815940857},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6323999762535095},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5972999930381775},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.5852000117301941},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5489000082015991},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5113000273704529},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48190000653266907},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.4659999907016754}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7771000266075134},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7682999968528748},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7623999714851379},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6467999815940857},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6323999762535095},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5972999930381775},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5852000117301941},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5489000082015991},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5113000273704529},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.507099986076355},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48190000653266907},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.4659999907016754},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.43220001459121704},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.40560001134872437},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.39169999957084656},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.3804999887943268},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.32510000467300415},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.30570000410079956},{"id":"https://openalex.org/C71681937","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object-class detection","level":5,"score":0.29030001163482666},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C29168087","wikidata":"https://www.wikidata.org/wiki/Q1026711","display_name":"Blob detection","level":5,"score":0.2766999900341034},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.258899986743927},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2026.3653616","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2026.3653616","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7403087019920349,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2103504761","https://openalex.org/W2117539524","https://openalex.org/W2194775991","https://openalex.org/W2963766909","https://openalex.org/W2981718299","https://openalex.org/W3013338555","https://openalex.org/W3034552520","https://openalex.org/W3035731588","https://openalex.org/W3096609285","https://openalex.org/W3204374989","https://openalex.org/W4214627427","https://openalex.org/W4312249431","https://openalex.org/W4312312588","https://openalex.org/W4322741918","https://openalex.org/W4385245566","https://openalex.org/W4386066362","https://openalex.org/W4386939303","https://openalex.org/W4402704573","https://openalex.org/W4402754006","https://openalex.org/W4403757566","https://openalex.org/W4405494998","https://openalex.org/W4406415424","https://openalex.org/W4409640680","https://openalex.org/W4410635266","https://openalex.org/W4411635474"],"related_works":[],"abstract_inverted_index":{"Object":[0],"detection":[1,36,47,150],"is":[2,12,27,112],"a":[3,57,106],"core":[4],"task":[5],"in":[6,15],"computer":[7],"vision,":[8],"yet":[9],"its":[10,153],"performance":[11],"severely":[13],"degraded":[14],"low-light":[16,52,137],"environments,":[17],"where":[18],"foreground":[19,80],"objects":[20],"blend":[21],"into":[22],"the":[23,87,125],"background,":[24],"feature":[25,126],"contrast":[26],"reduced,":[28],"and":[29,69,82,86,95,100,146,155],"object":[30,84],"boundaries":[31],"become":[32],"blurred,":[33],"ultimately":[34],"impairing":[35],"accuracy.":[37],"To":[38],"address":[39],"this":[40],"problem,":[41],"we":[42],"propose":[43],"FFE-DETR,":[44],"an":[45],"end-to-end":[46],"framework":[48],"specifically":[49],"designed":[50],"for":[51],"scenes.":[53],"The":[54,72],"model":[55],"incorporates":[56],"Frequency-Aware":[58],"Feature":[59,109],"Enhancer":[60],"that":[61,140],"applies":[62],"Laplacian":[63],"pyramid":[64],"decomposition":[65],"to":[66,78,114],"separate":[67],"low-frequency":[68,73],"high-frequency":[70,92],"components.":[71],"features":[74],"are":[75],"globally":[76],"modeled":[77],"enhance":[79],"saliency":[81],"emphasize":[83],"boundaries,":[85],"enhanced":[88],"representation":[89,127],"subsequently":[90],"guides":[91],"detail":[93],"restoration":[94],"noise":[96],"suppression,":[97],"yielding":[98],"clearer":[99],"more":[101],"discriminative":[102],"features.":[103],"In":[104],"addition,":[105],"Multi-Scale":[107],"Adaptive":[108],"Fusion":[110],"module":[111],"introduced":[113],"efficiently":[115],"integrate":[116],"shallow":[117],"texture":[118],"information":[119],"with":[120],"deep":[121],"semantic":[122],"cues,":[123],"enhancing":[124],"capability":[128],"across":[129],"different":[130],"scales.":[131],"Experimental":[132],"results":[133],"on":[134],"widely":[135],"used":[136],"benchmarks":[138],"demonstrate":[139],"FFE-DETR":[141],"consistently":[142],"outperforms":[143],"state-of-the-art":[144],"methods":[145],"achieves":[147],"significantly":[148],"superior":[149],"accuracy,":[151],"highlighting":[152],"effectiveness":[154],"robustness.":[156]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2026-01-14T00:00:00"}