{"id":"W4386598344","doi":"10.1109/icip49359.2023.10222331","title":"Retinex-based Image Denoising / Contrast Enhancement Using Gradient Graph Laplacian Regularizer","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Color constancy; Piecewise; Laplacian matrix; Computer science; Pixel; Artificial intelligence; Graph; Regularization (linguistics); Laplace operator; Mathematics; Noise reduction; Computation; Computer vision; Algorithm; Image (mathematics); Theoretical computer science","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008246714,0.0002693185,0.0002516641,0.0004379019,0.0002812447,0.0003670636,0.0008511905,0.00007162291,0.00008677763],"category_scores_gemma":[0.00005248107,0.0002573341,0.0001234815,0.001350726,0.0001114788,0.0006635665,0.000306796,0.0001520129,0.0001512451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001492805,"about_ca_system_score_gemma":0.00008758227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000553902,"about_ca_topic_score_gemma":0.00001164991,"domain_scores_codex":[0.9975363,0.0001050162,0.0004203448,0.0006644654,0.0005612969,0.0007125524],"domain_scores_gemma":[0.9986004,0.00007239207,0.0001450037,0.0008954674,0.0001620544,0.0001246396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001432073,0.0001022115,0.0001803372,0.00003689249,0.00002650504,0.0001132743,0.0002208385,0.00004337475,0.9745538,0.01346494,0.005324327,0.005919136],"study_design_scores_gemma":[0.0004810968,0.00009978632,0.0003307054,0.00009083305,0.0000111069,0.000005959407,0.00002560322,0.07671168,0.9181837,0.002585368,0.001097552,0.0003766052],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05270236,0.00002689946,0.9408288,0.001036297,0.0003562188,0.0004366552,0.000001806812,0.001795779,0.002815193],"genre_scores_gemma":[0.4801159,0.00001108041,0.5177072,0.0009641443,0.00006375901,0.00005480069,0.00001403216,0.00003270253,0.001036411],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4274135,"threshold_uncertainty_score":0.9999879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01988379626486282,"score_gpt":0.2713705764216916,"score_spread":0.2514867801568288,"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."}}