{"id":"W2324595411","doi":"","title":"Image restoration through microlocal analysis with smooth tight wavelet frames (Theoretical development and feasibility of mathematical analysis on the computer)","year":2002,"lang":"en","type":"article","venue":"Kyoto University Research Information Repository (Kyoto University)","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education, Culture, Sports, Science and Technology; National Science Foundation","keywords":"Wavelet; Microlocal analysis; Computer science; Mathematics; Artificial intelligence; Algorithm; Pattern recognition (psychology); Calculus (dental); Operator theory; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00114314,0.0001840008,0.0003539287,0.00153315,0.0009442181,0.0002940027,0.0009794909,0.0001327146,0.0001068361],"category_scores_gemma":[0.0001003017,0.0001485129,0.0001640638,0.004766314,0.000950043,0.001997261,0.0004357291,0.0004101259,0.00005263833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003323932,"about_ca_system_score_gemma":0.000166313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001009606,"about_ca_topic_score_gemma":0.000008979128,"domain_scores_codex":[0.9969912,0.001030015,0.0002942869,0.000377964,0.0009723962,0.0003341007],"domain_scores_gemma":[0.9973968,0.0006851046,0.0001999607,0.0007514362,0.0007922309,0.0001744377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001047868,0.0005543515,0.004167675,0.0001419337,0.002478183,0.0005273172,0.02372301,0.001050419,0.0004094417,0.9611248,0.0007835037,0.003991513],"study_design_scores_gemma":[0.00530881,0.002793785,0.02848111,0.0002152647,0.002315701,0.000106398,0.01161788,0.8862357,0.02259642,0.006437115,0.03229016,0.00160171],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.311403,0.000003994687,0.6591637,0.0006286823,0.00001575626,0.0003704813,0.000003565787,0.00005728965,0.02835353],"genre_scores_gemma":[0.9073761,0.00001463825,0.09140564,0.00010851,0.00001239256,6.606045e-7,0.00001093199,0.000004630424,0.001066532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9546877,"threshold_uncertainty_score":0.7262258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0381148089646441,"score_gpt":0.2684016571042389,"score_spread":0.2302868481395948,"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."}}