{"id":"W2146753780","doi":"10.1049/iet-ipr.2010.0408","title":"Wavelet-based image denoising using three scales of dependency","year":2012,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Complex wavelet transform; Wavelet; Artificial intelligence; Noise reduction; Non-local means; Pattern recognition (psychology); Additive white Gaussian noise; Image denoising; Mathematics; Image (mathematics); Wavelet transform; Dependency (UML); White noise; Video denoising; Tree (set theory); Computer science; Noise (video); Computer vision; Wavelet packet decomposition; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001563414,0.0003126485,0.0004065777,0.0002829664,0.0003830792,0.0005132638,0.0009391372,0.0001169624,0.00001880523],"category_scores_gemma":[0.000253558,0.0002953315,0.0001569895,0.0008045286,0.0002242457,0.00376234,0.0002729168,0.000282051,0.00002246304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008340157,"about_ca_system_score_gemma":0.0002632993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004716451,"about_ca_topic_score_gemma":0.000003535429,"domain_scores_codex":[0.9973547,0.0001971644,0.0005553764,0.0004568907,0.0006181903,0.0008176689],"domain_scores_gemma":[0.9982324,0.0001792512,0.0003756718,0.0006119018,0.0004061584,0.0001946088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000205909,0.0001489384,0.001328989,0.0003078513,0.00001168961,0.0000324385,0.0005415389,0.0000177052,0.7525691,0.0001889844,0.00002342865,0.2448088],"study_design_scores_gemma":[0.0009409761,0.0000503487,0.003646164,0.0004444527,0.00005687996,0.0001426798,0.00006786844,0.1598926,0.829542,0.004526994,0.00005068243,0.0006383674],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0865211,0.002029453,0.9097614,0.0001016408,0.0002528903,0.0001343459,0.000002292474,0.0001796343,0.00101723],"genre_scores_gemma":[0.4655784,0.000001551744,0.5341434,0.0001112151,0.0001236201,0.00000260147,0.000001096848,0.00002466656,0.00001344304],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3790573,"threshold_uncertainty_score":0.9999499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0369066361532803,"score_gpt":0.3155555772546787,"score_spread":0.2786489411013984,"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."}}