{"id":"W2146395906","doi":"10.1109/tcsvt.2010.2045806","title":"Video Denoising Using Motion Compensated 3-D Wavelet Transform With Integrated Recursive Temporal Filtering","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Wavelet transform; Wavelet; Artificial intelligence; Second-generation wavelet transform; Stationary wavelet transform; Computer vision; Wavelet packet decomposition; Discrete wavelet transform; Computer science; Lifting scheme; Video denoising; Harmonic wavelet transform; Motion compensation; Pattern recognition (psychology); Fast wavelet transform; Noise reduction; Mathematics; Video processing; Video tracking","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":[0.0005279384,0.0005979756,0.0006389292,0.000617411,0.0002066038,0.0005584023,0.0007704583,0.0007562263,0.0006061922],"category_scores_gemma":[0.0009956183,0.0002689309,0.0008328457,0.0006359424,0.0003272934,0.0007311685,0.0004238247,0.0005773993,0.0003421664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003033491,"about_ca_system_score_gemma":0.000412036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001487884,"about_ca_topic_score_gemma":0.001861375,"domain_scores_codex":[0.9997477,0.00004156382,0.00001461331,0.00004096941,0.0001384142,0.00001669026],"domain_scores_gemma":[0.9998068,0.00006213816,0.00002906403,0.00003234112,0.00006065973,0.000008988515],"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":[0.0002250028,0.00007421228,0.001029711,0.0003611161,0.0001279758,0.0003826533,0.0001918694,0.1104389,0.344674,0.02405011,0.002004767,0.5164397],"study_design_scores_gemma":[0.0000212892,0.0001347413,0.0007856337,0.00002470491,0.00006242041,0.0005813466,0.0000210279,0.9067726,0.07950647,0.004134101,0.007913491,0.00004231353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004106122,0.0002344755,0.9951002,0.00002369388,0.0000195206,0.00001587243,0.00001178999,0.0001604487,0.0003279115],"genre_scores_gemma":[0.09140719,0.0007898503,0.9063667,0.00005771841,0.00004985693,0.00005308483,0.0001105286,0.00006875666,0.001096282],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001487884,"threshold_uncertainty_score":0.002958477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02857171181112308,"score_gpt":0.2677441971697589,"score_spread":0.2391724853586358,"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."}}