{"id":"W2146640671","doi":"10.1109/icip.2009.5414423","title":"Nonlocal back-projection for adaptive image enlargement","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":131,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Innovation and Technology Fund","keywords":"Artificial intelligence; Computer vision; Computer science; Iterative reconstruction; Ringing artifacts; Image (mathematics); Projection (relational algebra); Process (computing); Iterative method; Image quality; Algorithm","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":[0.0001508968,0.0001045235,0.00009304729,0.00007080996,0.0000949062,0.0001085523,0.0004024255,0.00003288777,0.00001270677],"category_scores_gemma":[0.00002865975,0.00009106501,0.00004400555,0.0001977509,0.00002293995,0.001018046,0.00007495211,0.0000612197,0.00003589177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006332961,"about_ca_system_score_gemma":0.00003650536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003050406,"about_ca_topic_score_gemma":0.00000104539,"domain_scores_codex":[0.9991783,0.00001238007,0.0001363766,0.0003233451,0.0001271326,0.0002224821],"domain_scores_gemma":[0.9994542,0.00002485334,0.00005312061,0.0002755762,0.0001525517,0.00003970652],"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":[0.00003249523,0.0001687504,0.000003562249,0.00001179456,0.000006989257,0.000004131169,0.0001881568,0.000004234581,0.03886644,0.1089818,0.0187764,0.8329552],"study_design_scores_gemma":[0.0003702737,0.0007413498,0.00005418121,0.00001965694,0.000003640123,0.00001147396,0.00002535469,0.7149879,0.1625675,0.1111669,0.009790139,0.0002617029],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000009884352,0.00002647644,0.9862832,0.001213031,0.00007112346,0.0003130599,7.919551e-7,0.0006378162,0.01144456],"genre_scores_gemma":[0.0121465,0.000004334473,0.9858447,0.001009219,0.0000464865,0.00003408892,0.00000125242,0.000005327003,0.0009081244],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8326936,"threshold_uncertainty_score":0.3713523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02103643457817597,"score_gpt":0.3038121174192486,"score_spread":0.2827756828410727,"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."}}