{"id":"W1975558697","doi":"10.1155/2010/891703","title":"Emerging Methods for Color Image and Video Quality Enhancement","year":2010,"lang":"en","type":"article","venue":"EURASIP Journal on Image and Video Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Biometrics; Artificial intelligence; Computer science; Computer vision; Image enhancement; Image quality; Color image; Pattern recognition (psychology); Image processing; Image (mathematics)","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004134775,0.0003108335,0.0003941231,0.0002268166,0.001062585,0.001426123,0.0005484063,0.0000880248,0.0000282863],"category_scores_gemma":[0.000750297,0.000263819,0.00008698511,0.0002454363,0.0001684454,0.002695701,0.0002716524,0.0007438313,0.000004576035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000513765,"about_ca_system_score_gemma":0.0001345739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004604714,"about_ca_topic_score_gemma":0.000002105606,"domain_scores_codex":[0.9975503,0.0002513609,0.0007172378,0.0005869294,0.0003288054,0.0005653509],"domain_scores_gemma":[0.9979614,0.0004318458,0.0005821604,0.0003346498,0.00046641,0.000223524],"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":[0.000036819,0.00004168414,0.00006295808,0.0001096144,0.0000118132,0.00001065628,0.0005600433,8.408904e-8,0.5547936,0.0005714339,0.0005449114,0.4432563],"study_design_scores_gemma":[0.001494592,0.0005296227,0.00109845,0.0003820403,0.00004243904,0.0002225404,0.0001810059,0.01148696,0.9453486,0.01104781,0.02749435,0.0006715998],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04258046,0.0006542479,0.9521055,0.002568664,0.000433155,0.0003702583,0.000001416545,0.0001576138,0.001128719],"genre_scores_gemma":[0.1106118,0.0001848499,0.8871224,0.001360984,0.00031278,0.00007144625,0.000001003479,0.00002936646,0.0003053805],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4425848,"threshold_uncertainty_score":0.9999814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02936347525649839,"score_gpt":0.4061497491996857,"score_spread":0.3767862739431873,"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."}}