{"id":"W2125460212","doi":"10.1109/icip.2000.899568","title":"The iterative deconvolution of linearly blurred images using non-parametric stabilizing functions","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Deconvolution; Regularization (linguistics); Iterative method; Parametric statistics; Blind deconvolution; Propagation of uncertainty; Noise (video); Algorithm; Image restoration; Computer science; Mathematics; Mathematical optimization; Image (mathematics); Image processing; Artificial intelligence; Statistics","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":[0.0002270264,0.000110825,0.0001209274,0.0001739396,0.0003590348,0.0002274415,0.0004860907,0.00003437314,0.0000121486],"category_scores_gemma":[0.0002825689,0.00007974009,0.0000513274,0.001166255,0.0001348684,0.001338881,0.0001771889,0.0001267385,0.0000106111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006990665,"about_ca_system_score_gemma":0.00002959989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002017696,"about_ca_topic_score_gemma":0.000002575804,"domain_scores_codex":[0.9990199,0.00004666689,0.0002734358,0.0002575631,0.0001868794,0.0002155525],"domain_scores_gemma":[0.9987438,0.0002799702,0.0001674376,0.0004440112,0.0003276649,0.00003715207],"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":[0.00002613217,0.000642352,0.00379032,0.0001146904,0.0001094714,0.00001093464,0.003629299,0.001743186,0.4629923,0.01343635,0.005832883,0.5076721],"study_design_scores_gemma":[0.0001099325,0.00007183521,0.0002542073,0.0000251871,0.000005896654,0.000009807645,0.00005813226,0.9329949,0.0625458,0.003494607,0.0003075168,0.0001221852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004772243,0.0006756806,0.9924538,0.0001936754,0.0001198689,0.0001442438,0.000001373209,0.000203508,0.001435611],"genre_scores_gemma":[0.4405233,0.00002619802,0.5590611,0.00002507384,0.00001625865,0.000008256375,2.285215e-7,0.000005524437,0.0003340816],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9312517,"threshold_uncertainty_score":0.3251706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03308098640232934,"score_gpt":0.2836304319541779,"score_spread":0.2505494455518485,"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."}}