{"id":"W2118534572","doi":"10.1002/pamm.200700447","title":"Nonlocal‐means single‐frame image zooming","year":2007,"lang":"en","type":"article","venue":"PAMM","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Ontario Innovation Trust","keywords":"Zoom; Computer science; Artificial intelligence; Image (mathematics); Noise reduction; Computer vision; Frame (networking); Image processing; Noise (video); Scheme (mathematics); Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003377065,0.0004204333,0.0004800686,0.0003763312,0.0001538321,0.0002670481,0.0005361867,0.0004168321,0.002933857],"category_scores_gemma":[0.0008263317,0.0001745109,0.0003129051,0.0002520875,0.0003313898,0.0006704716,0.0005291919,0.0005019892,0.000523622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001844563,"about_ca_system_score_gemma":0.0001772241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004057288,"about_ca_topic_score_gemma":0.0009537639,"domain_scores_codex":[0.9998674,0.00002581307,0.000004384329,0.00003586703,0.00005708531,0.000009415228],"domain_scores_gemma":[0.9997104,0.00009729664,0.00003919486,0.00007618337,0.00005907568,0.00001783076],"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":[0.0003969239,0.00008051857,0.0005055564,0.0002970525,0.00007843837,0.0002155491,0.000318443,0.06075374,0.4032691,0.01497667,0.00315466,0.5159533],"study_design_scores_gemma":[0.00003656119,0.0001986487,0.002195763,0.00002803206,0.00003406357,0.0004857224,0.00005892171,0.7613456,0.2162463,0.007932229,0.0113963,0.00004193412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04164915,0.0003498037,0.9552676,0.00009318421,0.00005249901,0.00003382222,0.00003169477,0.0005126863,0.002009559],"genre_scores_gemma":[0.3414253,0.0004445738,0.6515221,0.0001291099,0.00008762681,0.00005862884,0.0001141049,0.0001338885,0.00608467],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002933857,"threshold_uncertainty_score":0.009814739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0223530941986809,"score_gpt":0.2849535577118325,"score_spread":0.2626004635131515,"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."}}