{"id":"W2140959962","doi":"10.1109/ccece.1997.608267","title":"Multiscale-based image enhancement","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wavelet; Wavelet transform; Histogram; Wavelet packet decomposition; Artificial intelligence; Noise (video); Stationary wavelet transform; Pattern recognition (psychology); Image (mathematics); Filter (signal processing); Computer science; Second-generation wavelet transform; Computer vision; Contrast (vision); Discrete wavelet transform; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002120703,0.00008025087,0.00008187384,0.00005060427,0.00007221616,0.0001423171,0.0004310377,0.00002267405,0.0009407954],"category_scores_gemma":[0.00002456398,0.00006552842,0.00004847836,0.0001738106,0.00002515879,0.0002745644,0.00006634,0.00005736896,0.0009210418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001661081,"about_ca_system_score_gemma":0.000006588822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001042451,"about_ca_topic_score_gemma":6.813527e-7,"domain_scores_codex":[0.9991956,0.00007085378,0.0001212303,0.0002260171,0.0001856402,0.0002006282],"domain_scores_gemma":[0.9994088,0.00007425894,0.00002424342,0.0003893739,0.00004271055,0.00006065176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006931752,0.0004073746,0.00005511546,0.00001965438,0.00001345446,0.0001370029,0.0004627998,0.00003347805,0.3407559,0.010966,0.03749335,0.609649],"study_design_scores_gemma":[0.00076283,0.00007548762,0.0001429102,0.000008268362,0.000002407525,0.000004610264,0.000002137721,0.42164,0.5627915,0.0006546954,0.01373893,0.0001762075],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006752759,0.00007105598,0.9497674,0.001306449,0.000168407,0.00006055718,1.574286e-7,0.0001415725,0.0478091],"genre_scores_gemma":[0.08601337,0.000003251397,0.9008707,0.001876298,0.00003225085,0.000006831201,2.870175e-7,0.000004365302,0.01119268],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6094728,"threshold_uncertainty_score":0.9999725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03019273057107177,"score_gpt":0.2741317197408855,"score_spread":0.2439389891698137,"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."}}