{"id":"W2111784773","doi":"10.1109/icme.2006.262629","title":"Cost-Effective Sharpening of Single-Sensor Camera Images","year":2006,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Sharpening; Demosaicing; Computer vision; Artificial intelligence; RGB color model; Bayer filter; Color filter array; Pipeline (software); Computer science; Image sensor; Color image; Digital camera; Filter (signal processing); Image (mathematics); Image processing; Color gel; Materials science","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":[0.0003515697,0.0001070667,0.0001802566,0.00009799008,0.0000621496,0.0001076609,0.0003687886,0.0000361129,0.00003481732],"category_scores_gemma":[0.00006494245,0.00009017216,0.00007347577,0.0002875557,0.00005482067,0.0003635304,0.0001191419,0.00007622667,0.00003218906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000025658,"about_ca_system_score_gemma":0.00001812269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002754102,"about_ca_topic_score_gemma":0.000003762869,"domain_scores_codex":[0.9990026,0.0001448459,0.000194703,0.0002552198,0.0001889793,0.0002136957],"domain_scores_gemma":[0.9991189,0.0003365516,0.00008059494,0.0002994759,0.0001307488,0.00003374698],"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":[0.00001532439,0.000114651,0.0004588179,0.00001725035,0.0000137002,0.00005157461,0.0002204363,0.0002734837,0.7473242,0.008144629,0.002906437,0.2404596],"study_design_scores_gemma":[0.0005001819,0.0001188604,0.00407185,0.00002380008,0.00000762218,0.00002680372,0.00001920899,0.005612256,0.9841679,0.002789538,0.0024835,0.0001784979],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008910672,0.0001110026,0.9524296,0.0001666188,0.00014174,0.0002821899,0.000001529921,0.0001127598,0.03784392],"genre_scores_gemma":[0.4633306,9.33299e-7,0.5334798,0.0001921336,0.00007470741,0.00001832589,0.000001233551,0.000008538933,0.002893766],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4544199,"threshold_uncertainty_score":0.3677114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03168916475820013,"score_gpt":0.2933657298570261,"score_spread":0.261676565098826,"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."}}