{"id":"W3183083473","doi":"","title":"Improved Demosaicking in the Frequency Domain by Restoration Filtering of the LCC Bands","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Demosaicing; Bayer filter; Color filter array; Computer vision; Artificial intelligence; Image restoration; Frequency domain; Adaptive filter; Filter (signal processing); Computer science; Mathematics; Interpolation (computer graphics); Image (mathematics); Color image; Algorithm; Image processing; Color gel; 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":[0.001507594,0.0002384046,0.0003093192,0.00008092479,0.0001639993,0.0001197869,0.002334735,0.0001390557,0.000002092097],"category_scores_gemma":[0.0005884186,0.0001541949,0.0004268166,0.0006397556,0.000244941,0.00082804,0.0002535137,0.000375272,2.738296e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001171681,"about_ca_system_score_gemma":0.0000520996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003346023,"about_ca_topic_score_gemma":3.606731e-7,"domain_scores_codex":[0.9978871,1.451617e-7,0.000693956,0.0003344098,0.0007486933,0.0003357271],"domain_scores_gemma":[0.9983855,0.0002439392,0.000431694,0.0001250488,0.0007647219,0.00004907774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002308585,0.00006512621,0.0004452406,0.0001147864,0.00006034652,1.618499e-7,0.001225462,0.00003801049,0.7871696,0.2092895,0.00132597,0.0002427161],"study_design_scores_gemma":[0.003229885,0.0006682,0.006583355,0.0007958318,0.0001051809,0.0001467453,0.0020255,0.1055869,0.8562524,0.0216029,0.002171857,0.0008312449],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897262,0.0001285959,0.005393121,0.002944112,0.0002349168,0.0004146678,0.00001121679,0.00003735135,0.001109773],"genre_scores_gemma":[0.6714785,0.00004733325,0.3279116,0.000204359,0.0001697261,0.00007836304,0.000002330337,0.00002702848,0.00008080774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3225185,"threshold_uncertainty_score":0.6287886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01457057696508596,"score_gpt":0.2361024820596778,"score_spread":0.2215319050945918,"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."}}