{"id":"W2110248449","doi":"10.1109/icassp.2006.1660476","title":"Homogeneity-Based Directional Wiener Filtering of Video Noise","year":2006,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Wiener filter; Computer science; Artificial intelligence; Computer vision; Weighting; Bilateral filter; Median filter; Noise reduction; Filter (signal processing); Gaussian noise; Homogeneity (statistics); Noise measurement; Noise (video); Homogeneous; Additive white Gaussian noise; White noise; Image (mathematics); Mathematics; Image processing; Acoustics","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":[0.0005030859,0.0004598491,0.0005070444,0.0006472308,0.0002189513,0.0003938618,0.0004107213,0.0003674808,0.001018492],"category_scores_gemma":[0.001110445,0.0002355682,0.000579878,0.0003575059,0.0002917987,0.0006197028,0.0004645271,0.0003548844,0.000569544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002926716,"about_ca_system_score_gemma":0.0002595707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00111162,"about_ca_topic_score_gemma":0.001999208,"domain_scores_codex":[0.9996717,0.00004647415,0.0000179891,0.00006890635,0.0001668737,0.00002812977],"domain_scores_gemma":[0.9997184,0.00009507073,0.00004005424,0.00003550433,0.00009352942,0.00001738663],"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":[0.0004282984,0.00007274718,0.001253844,0.0002581865,0.0001035077,0.0001429073,0.0001460543,0.0307747,0.4686961,0.01288753,0.001167869,0.4840682],"study_design_scores_gemma":[0.00006051971,0.0004758479,0.005982865,0.00003965779,0.0002169329,0.0006247445,0.00006590902,0.6260946,0.3439681,0.006592924,0.01579916,0.00007881675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02473939,0.0004903284,0.9727743,0.00004804347,0.00003704854,0.00002253692,0.00002876255,0.0002819947,0.001577593],"genre_scores_gemma":[0.3731048,0.001451654,0.6149479,0.0001027707,0.0001833416,0.00008282796,0.0002440101,0.0001913378,0.009691275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00111162,"threshold_uncertainty_score":0.00340718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01531926063863515,"score_gpt":0.2519988922174805,"score_spread":0.2366796315788453,"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."}}