{"id":"W2545011001","doi":"10.1109/acssc.2012.6489324","title":"A novel de-interlacing method based on locally-adaptive Nonlocal-means","year":2012,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Interlacing; Pixel; Computer vision; Kernel (algebra); Computer science; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Set (abstract data type); Algorithm; Mathematics; Discrete mathematics","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":[0.0008455898,0.0009940364,0.001248828,0.001338696,0.0007203206,0.0007556882,0.002205019,0.001098623,0.001819605],"category_scores_gemma":[0.001852901,0.000560788,0.001200618,0.001196839,0.0005893576,0.001732528,0.001110723,0.001510667,0.001169689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006734295,"about_ca_system_score_gemma":0.0009223699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002754336,"about_ca_topic_score_gemma":0.004275041,"domain_scores_codex":[0.9990569,0.0001437344,0.00006232275,0.000255455,0.0004282209,0.00005342977],"domain_scores_gemma":[0.999162,0.000177375,0.00009621536,0.000172085,0.0003400782,0.00005225399],"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":[0.0002746572,0.0001551047,0.0005769299,0.0002973269,0.0001781451,0.0001270816,0.0003209315,0.06372666,0.1180055,0.01102207,0.004872433,0.8004431],"study_design_scores_gemma":[0.00003717453,0.0001182486,0.0005825543,0.00001816928,0.00006161007,0.0002714894,0.00004604358,0.9279673,0.0521991,0.004288205,0.01433622,0.00007388048],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002440193,0.0001611354,0.9967063,0.00003368951,0.00005519283,0.00002164728,0.00001369558,0.0002778317,0.0002904102],"genre_scores_gemma":[0.02957354,0.0002244464,0.9672035,0.0000641572,0.0000839917,0.00008005889,0.0001037705,0.000145022,0.002521523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002754336,"threshold_uncertainty_score":0.006087184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03756581128185667,"score_gpt":0.3151496013103686,"score_spread":0.2775837900285119,"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."}}