{"id":"W2140895162","doi":"10.1109/tce.2005.1468018","title":"Fast video demosaicking solution for mobile phone imaging applications","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Mobile phone; Computer vision; Demosaicing; Video processing; Video capture; Image processing; Image (mathematics); Color image; Telecommunications","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":[0.0004305342,0.0002078394,0.0001902763,0.0001956053,0.0006504392,0.0001739547,0.0004857724,0.00007327933,0.00001972642],"category_scores_gemma":[0.000004586112,0.000228251,0.0001687961,0.0004687255,0.00005996293,0.0005699638,0.00000315994,0.0003317593,0.0001075219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002583214,"about_ca_system_score_gemma":0.0002221373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001264953,"about_ca_topic_score_gemma":0.00004582514,"domain_scores_codex":[0.9982998,0.00008574352,0.0003094127,0.0004979958,0.0002164344,0.0005906216],"domain_scores_gemma":[0.998816,0.0002958603,0.00009217422,0.0005377971,0.0001613807,0.00009686159],"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":[0.00002302371,0.0001478277,0.000001501642,0.00001011602,0.00003526136,5.866475e-7,0.0001415011,0.006472225,0.03380625,0.0007181662,0.0003735838,0.95827],"study_design_scores_gemma":[0.001375801,0.0001088862,0.000003841324,0.00002188096,0.00008428415,0.00006576179,0.00001458694,0.523048,0.3039682,0.001420369,0.169443,0.0004452625],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000268407,0.002047799,0.9954077,0.0008442016,0.0002571424,0.0006543272,0.00001207,0.0003275393,0.0001808315],"genre_scores_gemma":[0.6266961,0.0003186885,0.3699712,0.0009380482,0.0001297604,0.001182603,0.000005755739,0.00004332659,0.0007145551],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9578247,"threshold_uncertainty_score":0.9307804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302593188500966,"score_gpt":0.2777852023029738,"score_spread":0.2647592704179641,"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."}}