{"id":"W2245295779","doi":"10.1007/978-3-319-20801-5_7","title":"Structural Similarity Optimized Wiener Filter: A Way to Fight Image Noise","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Wiener filter; Similarity (geometry); Structural similarity; Image (mathematics); Filter (signal processing); Noise (video); Computer science; Computer vision; Artificial intelligence; Mathematics; Algorithm","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":[0.0003947383,0.000584353,0.0007270142,0.0005463051,0.0002652356,0.000862706,0.0006430828,0.001158554,0.002842761],"category_scores_gemma":[0.000926934,0.0003437008,0.0006758954,0.0006152326,0.0004625385,0.001102887,0.0008497074,0.0009708386,0.001589838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002497897,"about_ca_system_score_gemma":0.0004526122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004605018,"about_ca_topic_score_gemma":0.0008845192,"domain_scores_codex":[0.999729,0.00003677337,0.00001429695,0.00005855384,0.0001386642,0.0000227111],"domain_scores_gemma":[0.999713,0.00007553111,0.0000315127,0.00005792939,0.0000985631,0.00002353626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003278983,0.0001769098,0.0006027953,0.0002829211,0.0001636138,0.0001436521,0.0001442926,0.04783776,0.2812567,0.1070609,0.006428419,0.5555741],"study_design_scores_gemma":[0.00003674155,0.000228094,0.0007716409,0.00003210473,0.0001316058,0.0005761285,0.0000439634,0.8293099,0.1098157,0.0415081,0.01749675,0.0000492423],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004903603,0.0002971376,0.9930253,0.00005596628,0.00006023053,0.000009537883,0.00001704386,0.0002365581,0.001394648],"genre_scores_gemma":[0.1366098,0.001303744,0.8380138,0.0002166752,0.000189812,0.00005777964,0.0001671476,0.0003743926,0.02306678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002842761,"threshold_uncertainty_score":0.009509981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03405695893907894,"score_gpt":0.289147186643087,"score_spread":0.2550902277040081,"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."}}