{"id":"W4410884675","doi":"10.1016/j.engappai.2025.111115","title":"A codebook-driven approach for low-light image enhancement","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Shenzhen Science and Technology Innovation Program; National Natural Science Foundation of China","keywords":"Computer science; Codebook; Image enhancement; Image (mathematics); Artificial intelligence; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002367115,0.000153071,0.0001837039,0.0002436845,0.00008803153,0.00007384713,0.001051616,0.00005483478,0.000005808368],"category_scores_gemma":[0.00005459256,0.0001695038,0.00008137557,0.0006193112,0.00004725964,0.0001965098,0.0001722664,0.00009460696,0.00001435001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006301014,"about_ca_system_score_gemma":0.00005269759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005917785,"about_ca_topic_score_gemma":5.683538e-7,"domain_scores_codex":[0.9987381,0.000008793811,0.0004626314,0.0003958383,0.0001514229,0.0002432261],"domain_scores_gemma":[0.9988035,0.00009622624,0.00009678216,0.0007474296,0.0002173822,0.00003866511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000004911743,0.000206468,9.004888e-7,0.0001594114,0.00002415111,1.381288e-7,0.0001502044,0.004833163,0.2869399,0.6328757,0.0003588177,0.07444625],"study_design_scores_gemma":[0.00001086545,0.00002050592,0.000001212333,0.00002555001,0.000005125289,2.048719e-7,0.00001237103,0.3985687,0.5948823,0.004512362,0.001867581,0.00009324378],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001141899,0.00007924909,0.9962907,0.0002634145,0.000082468,0.001168258,0.000004650814,0.0003281711,0.001668858],"genre_scores_gemma":[0.2210236,0.00001348213,0.7766849,0.00003628909,0.00003211171,0.001995677,0.000007373176,0.000009995341,0.0001965384],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6283633,"threshold_uncertainty_score":0.6912163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01264992838129827,"score_gpt":0.2771708479782732,"score_spread":0.264520919596975,"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."}}