{"id":"W2158155425","doi":"10.1007/978-3-540-30125-7_71","title":"Motion-Compensated Wavelet Video Denoising","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wavelet; Computer science; Noise reduction; Artificial intelligence; Video denoising; Estimator; Motion estimation; Kalman filter; Computer vision; Noise (video); Wavelet transform; Pattern recognition (psychology); Algorithm; Mathematics; Video processing; Video tracking; Image (mathematics); Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001652095,0.0004724019,0.0003862561,0.0004011075,0.00009262061,0.0003441674,0.0004335372,0.0005182966,0.004316864],"category_scores_gemma":[0.0004079017,0.0002208962,0.000278623,0.0004650074,0.000229603,0.0004569457,0.0002975738,0.0005425458,0.002568046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001425585,"about_ca_system_score_gemma":0.0001207279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003370277,"about_ca_topic_score_gemma":0.0005832771,"domain_scores_codex":[0.9999212,0.000006967634,0.000003369016,0.00001699009,0.00004574908,0.000005668856],"domain_scores_gemma":[0.999904,0.00002396366,0.000007458645,0.00001830584,0.00004136836,0.000004811523],"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":[0.0001247745,0.00005096135,0.0001492171,0.0002412079,0.00003502094,0.0001493812,0.00005894157,0.01849629,0.2787936,0.02168454,0.01020616,0.6700099],"study_design_scores_gemma":[0.00002304354,0.0001863475,0.001450525,0.0000885816,0.00009417393,0.001468657,0.00003818577,0.5357785,0.3558376,0.0178408,0.08715601,0.00003754854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009515817,0.002761802,0.9764454,0.0001511197,0.0002583583,0.00001793625,0.00006950609,0.0004248415,0.01035518],"genre_scores_gemma":[0.1462403,0.009813136,0.6834008,0.0002479136,0.000470398,0.0000486063,0.0008031345,0.0005476841,0.1584281],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004316864,"threshold_uncertainty_score":0.01444137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02112498679464196,"score_gpt":0.2625420182531446,"score_spread":0.2414170314585026,"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."}}