{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001315239,0.0001263276,0.0001436351,0.0001101356,0.0001276749,0.0002304954,0.0006261633,0.00006284873,0.00002250622],"category_scores_gemma":[0.000174673,0.0001180566,0.00007689262,0.0003703794,0.00005509357,0.0004341775,0.0001689593,0.0001725517,0.0002681001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004820429,"about_ca_system_score_gemma":0.00002834535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002624298,"about_ca_topic_score_gemma":0.000007544569,"domain_scores_codex":[0.9986812,0.00007778095,0.0002215603,0.0003150256,0.0002642665,0.0004400987],"domain_scores_gemma":[0.9989675,0.0002971781,0.00005821051,0.0004771958,0.00007978326,0.0001201111],"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":[0.000014952,0.00009204555,0.00009251097,0.00001391664,0.00001130604,0.0003397138,0.001064972,0.000005094845,0.4679795,0.00617583,0.001135065,0.5230751],"study_design_scores_gemma":[0.002225694,0.0004560886,0.005331467,0.0001580711,0.00003760914,0.0003654427,0.0002161226,0.221666,0.6231214,0.02201878,0.1228725,0.001530821],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00285016,0.0001022382,0.9526806,0.0003674644,0.0005020237,0.00006025691,3.577426e-7,0.0001948142,0.04324201],"genre_scores_gemma":[0.1283601,0.000002564148,0.8691932,0.001046587,0.0002263827,0.000001334916,7.647137e-7,0.0000134512,0.001155577],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5215443,"threshold_uncertainty_score":0.4814208,"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."}}