{"id":"W4415697246","doi":"10.18280/ts.420530","title":"Hybrid Deep Learning Approach for Low-Light Image Enhancement Based on Attention-Guided Residual Networks","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Deep learning; Residual; Image enhancement; Image (mathematics); Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003185397,0.001119767,0.0009376768,0.0008018239,0.001303576,0.001248909,0.002142614,0.000241256,0.0006758554],"category_scores_gemma":[0.0001159435,0.001207527,0.0006082399,0.0009399708,0.0002478541,0.0009113147,0.0005555801,0.0008879034,0.00003966697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007450095,"about_ca_system_score_gemma":0.0003593097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001250337,"about_ca_topic_score_gemma":0.000001674501,"domain_scores_codex":[0.9916236,0.0006973187,0.001982977,0.002362919,0.001483392,0.001849839],"domain_scores_gemma":[0.9966241,0.0004009075,0.0007832548,0.001239014,0.0006853678,0.0002673629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002764398,0.0132055,0.0006272239,0.003518984,0.001624438,0.0001847793,0.0010899,0.239808,0.1388768,0.02476113,0.1680812,0.4054576],"study_design_scores_gemma":[0.003538704,0.00150659,0.0001821327,0.0007239062,0.0001955368,0.000002582836,0.00003258693,0.8522787,0.1371294,0.0003187589,0.003149948,0.0009410832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001822109,0.0003598614,0.9786599,0.001750563,0.0009786551,0.004590984,0.00001588398,0.0006136179,0.01120843],"genre_scores_gemma":[0.7910203,0.0000760152,0.1998226,0.002449445,0.0006831498,0.002050856,0.0003256042,0.00009160623,0.003480497],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7891982,"threshold_uncertainty_score":0.9999966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01220045627224457,"score_gpt":0.2571792063438673,"score_spread":0.2449787500716228,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}