{"id":"W2112545418","doi":"10.1109/tip.2006.881992","title":"Translation-Invariant Contourlet Transform and Its Application to Image Denoising","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Contourlet; Invariant (physics); Wavelet transform; Filter bank; Noise reduction; Artificial intelligence; Image denoising; Pattern recognition (psychology); Mathematics; Redundancy (engineering); Wavelet; Filter (signal processing); Computer science; Algorithm; Computer vision","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":[0.0004671951,0.0003591045,0.0003067129,0.000519213,0.0001387967,0.000427249,0.0003602435,0.0006791768,0.001005039],"category_scores_gemma":[0.001581345,0.0002019647,0.0004366392,0.0007930532,0.0005761398,0.0005592934,0.0003124123,0.0006705061,0.0002829449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002065944,"about_ca_system_score_gemma":0.0002770371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004374981,"about_ca_topic_score_gemma":0.0003987424,"domain_scores_codex":[0.9998456,0.00002737145,0.00001027744,0.00002909067,0.0000764404,0.00001127661],"domain_scores_gemma":[0.9996322,0.0001479949,0.00004371415,0.00006796003,0.00009427518,0.0000137747],"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":[0.0001348255,0.00005892004,0.0007971878,0.0002323569,0.00005906176,0.000446282,0.0002447827,0.1156584,0.1534778,0.1260602,0.0019908,0.6008393],"study_design_scores_gemma":[0.00001186981,0.0001050571,0.0008089572,0.00001673341,0.00003132342,0.0004776648,0.00002933689,0.9077349,0.05467857,0.02496043,0.01112375,0.00002132347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007685167,0.000485571,0.9903806,0.0001042186,0.00003950187,0.00001198977,0.000009481506,0.00007831805,0.001205218],"genre_scores_gemma":[0.1808699,0.002922487,0.8121924,0.00008779121,0.0001693736,0.00004954743,0.00007502709,0.00006170365,0.003571713],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001005039,"threshold_uncertainty_score":0.003362238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01614987512096629,"score_gpt":0.2776312431378795,"score_spread":0.2614813680169132,"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."}}