{"id":"W2048764473","doi":"10.1109/newcas.2014.6933977","title":"Image denoising in wavelet domain using the vector-based hidden Markov model","year":2014,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Wavelet; Pattern recognition (psychology); Estimator; Computer science; Artificial intelligence; Noise reduction; Wavelet transform; Hidden Markov model; Mathematics; Algorithm; 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":[0.0009110386,0.000380824,0.0007602184,0.0003587016,0.000192106,0.0004694902,0.0007137661,0.0006263595,0.0004527475],"category_scores_gemma":[0.001708437,0.0003078833,0.0008104564,0.0004770272,0.000377886,0.0009155841,0.0003619003,0.0008843097,0.0001982184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003528186,"about_ca_system_score_gemma":0.0004786868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002627783,"about_ca_topic_score_gemma":0.002500751,"domain_scores_codex":[0.9996921,0.00008176914,0.00001906269,0.00006176934,0.000121764,0.00002349733],"domain_scores_gemma":[0.9995432,0.0002748082,0.00005410138,0.00003847188,0.00008049282,0.000008951369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001612208,0.00006831695,0.002055692,0.0002667454,0.0001480831,0.0002046978,0.0002003309,0.7155393,0.03678323,0.04834884,0.001180513,0.1950431],"study_design_scores_gemma":[0.000003230969,0.0000197685,0.000205619,0.000005544391,0.00001335949,0.00003894916,0.00000547508,0.993112,0.002786986,0.003403248,0.0003983821,0.00000754658],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005997523,0.0002172186,0.9933751,0.00004764606,0.00001379095,0.000006069128,0.00001172459,0.00009527618,0.0002356822],"genre_scores_gemma":[0.4980785,0.002279392,0.4951666,0.0001249931,0.0001015007,0.00008254623,0.0002360702,0.00007645907,0.003854045],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002627783,"threshold_uncertainty_score":0.005225003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02510536481584994,"score_gpt":0.2828313988337279,"score_spread":0.257726034017878,"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."}}