{"id":"W2006489877","doi":"10.1117/12.765199","title":"Improving the SNR during color image processing while preserving the appearance of clipped pixels","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Artificial intelligence; Color balance; Computer vision; Pixel; Computer science; Color image; Color depth; Color histogram; Color correction; Color normalization; Luminance; Image processing; Color difference; Image (mathematics); Enhanced Data Rates for GSM Evolution","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":[0.0006366977,0.0004042653,0.00047963,0.00008665286,0.0003099661,0.0001373481,0.001540474,0.0001528599,0.000009020579],"category_scores_gemma":[0.0004832543,0.0002736571,0.0005824778,0.0005185122,0.0005458758,0.0007677273,0.0002610066,0.0005660025,0.000001614624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001379607,"about_ca_system_score_gemma":0.000040836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001939308,"about_ca_topic_score_gemma":3.440279e-7,"domain_scores_codex":[0.9973778,5.049612e-8,0.0008735945,0.0003683627,0.0008029744,0.0005771963],"domain_scores_gemma":[0.997999,0.0001572203,0.0004172774,0.0001320593,0.00120202,0.0000924892],"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":[0.00007706792,0.00006670279,0.0004627025,0.001718112,0.0003016344,2.821038e-7,0.001329324,0.002274409,0.9752295,0.01713942,0.001070414,0.0003304206],"study_design_scores_gemma":[0.001595242,0.0001508711,0.004884024,0.001046608,0.0002351047,0.0001096079,0.003862835,0.3491916,0.6364227,0.0004786456,0.001335555,0.0006872346],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952862,0.0005471598,0.0001074195,0.001156478,0.0002348537,0.0006805809,0.00002768482,0.0002022459,0.001757338],"genre_scores_gemma":[0.9817875,0.000172881,0.01711558,0.00004966352,0.0004491902,0.000140876,0.000002282647,0.0001065039,0.0001755075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3469172,"threshold_uncertainty_score":0.9999716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009792958115116764,"score_gpt":0.2083074246134786,"score_spread":0.1985144664983618,"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."}}