{"id":"W2119167889","doi":"10.1109/tip.2011.2107524","title":"Least-Squares Luma–Chroma Demultiplexing Algorithm for Bayer Demosaicking","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Digital Image Processing Techniques","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Toronto","funders":"","keywords":"Demosaicing; Algorithm; Computer science; Artificial intelligence; Image processing; Computer vision; Mathematics; Pattern recognition (psychology); Image (mathematics); Color image","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":[0.0008246176,0.0006647548,0.0005018043,0.0006147657,0.0003977221,0.0005759861,0.0006134947,0.0005975868,0.002906295],"category_scores_gemma":[0.001793668,0.0002755803,0.0003691686,0.0006324589,0.0003470757,0.0005797457,0.0005027106,0.0008466129,0.001453298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004834858,"about_ca_system_score_gemma":0.0008953256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001320548,"about_ca_topic_score_gemma":0.003084708,"domain_scores_codex":[0.9996989,0.00006135597,0.00001588235,0.00005570192,0.0001468708,0.00002139304],"domain_scores_gemma":[0.999359,0.0001958684,0.0000555329,0.0001248479,0.0002394314,0.00002538131],"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":[0.0002790238,0.00008453163,0.0008792352,0.0002271406,0.00008157099,0.00008551387,0.0001581037,0.08417287,0.1735406,0.01429123,0.003802186,0.7223979],"study_design_scores_gemma":[0.00002802854,0.0001328005,0.001290788,0.00002348086,0.00004189392,0.0002951387,0.00004588509,0.8514584,0.1280486,0.004362978,0.01422503,0.0000469365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003104591,0.00008405393,0.995739,0.00002989294,0.00001466068,0.00002036539,0.00002648832,0.0003877507,0.0005932382],"genre_scores_gemma":[0.02877696,0.0001149778,0.9695033,0.00003048094,0.000013089,0.00003605215,0.00009323975,0.00005829022,0.001373505],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002906295,"threshold_uncertainty_score":0.009722531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0432920966998345,"score_gpt":0.2804801042245686,"score_spread":0.2371880075247341,"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."}}