{"id":"W2126994945","doi":"10.1109/ccece.2004.1345233","title":"Local correlation based CFA interpolation","year":2004,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Bell (Canada)","funders":"","keywords":"Demosaicing; Interpolation (computer graphics); Color filter array; Computer vision; Artificial intelligence; Color difference; Computer science; Stairstep interpolation; Image scaling; Bayer filter; Bilinear interpolation; Mathematics; Enhanced Data Rates for GSM Evolution; Color image; Image (mathematics); Color gel; Multivariate interpolation; Image processing; Layer (electronics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002289092,0.00005131746,0.00004946159,0.00006452579,0.00005221477,0.00007995931,0.0001864136,0.00003270449,0.00003298283],"category_scores_gemma":[0.00002471757,0.00004429199,0.00002988632,0.0001903159,0.00001812418,0.0003904591,0.00003074763,0.00005863118,0.0001373643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003810415,"about_ca_system_score_gemma":0.00005158587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004441138,"about_ca_topic_score_gemma":0.000004363742,"domain_scores_codex":[0.9994941,0.00004381735,0.0001007215,0.000140635,0.00012535,0.00009535097],"domain_scores_gemma":[0.9996684,0.00004802637,0.0000256178,0.0001824992,0.00004269281,0.00003282701],"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":[0.00003485196,0.0001256926,0.0003551236,0.000008513768,0.000008730785,0.00003434144,0.0007370168,0.08420147,0.01202476,0.4035634,0.0008366617,0.4980694],"study_design_scores_gemma":[0.0006503422,0.00006045565,0.001779114,0.00001329172,0.000001970799,0.000006521535,0.000007717279,0.9566122,0.01641149,0.02389587,0.000471318,0.00008965695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006532184,0.00001141799,0.988687,0.0006748124,0.0002488036,0.00004259682,7.340172e-8,0.0001406021,0.009541493],"genre_scores_gemma":[0.6101871,1.248044e-7,0.3890028,0.0006175667,0.00001287384,9.161002e-7,0.000001017379,0.00000191736,0.000175737],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8724108,"threshold_uncertainty_score":0.1806175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01700622593131174,"score_gpt":0.2703964065730988,"score_spread":0.2533901806417871,"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."}}