{"id":"W2171648450","doi":"10.1109/tce.2004.1277871","title":"A novel cost effective demosaicing approach","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Demosaicing; Interpolation (computer graphics); Computer vision; Artificial intelligence; Color filter array; Computer science; Color difference; Color correction; Process (computing); Enhanced Data Rates for GSM Evolution; Color image; Image processing; Image (mathematics); Color gel; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002496507,0.0002652128,0.0002201778,0.0002164787,0.000299661,0.0001227151,0.0006289427,0.0001120739,0.000006548667],"category_scores_gemma":[0.00000551226,0.0002735116,0.0001214875,0.0006549142,0.00007960502,0.0005092006,0.000004218879,0.0006125973,0.0000683004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006107789,"about_ca_system_score_gemma":0.0002721512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005537524,"about_ca_topic_score_gemma":0.00005416217,"domain_scores_codex":[0.9982157,0.00004881391,0.0002361179,0.0005684054,0.0003149722,0.0006159857],"domain_scores_gemma":[0.9989962,0.0001021768,0.00007678688,0.0006339495,0.00009677857,0.00009411119],"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":[0.00007672927,0.002145933,0.000005040644,0.00005408704,0.0003422765,0.00001459106,0.0009469348,0.02572675,0.1815256,0.0261774,0.0001798006,0.7628048],"study_design_scores_gemma":[0.001465437,0.0003767532,0.00001456263,0.00004430659,0.00004848192,0.0001107007,0.00001068356,0.01958487,0.9739153,0.001051929,0.002914385,0.0004625483],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001202326,0.0002216638,0.9950557,0.0002340475,0.0002466525,0.001274323,0.000005476766,0.0008164824,0.0009432936],"genre_scores_gemma":[0.8329176,0.0001078286,0.1656061,0.0004670666,0.00001537874,0.0007338055,0.000001746336,0.00002971961,0.0001208308],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8317152,"threshold_uncertainty_score":0.9999717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01207367660902718,"score_gpt":0.2495622807884047,"score_spread":0.2374886041793776,"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."}}