{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005047647,0.0006707982,0.0005836681,0.0004870512,0.0002486738,0.0004104892,0.0009376013,0.0005584121,0.002249324],"category_scores_gemma":[0.001168269,0.0003027401,0.0005031324,0.000420495,0.0005118999,0.0009817282,0.0009026023,0.000810024,0.0009304548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001950713,"about_ca_system_score_gemma":0.0002581452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003229207,"about_ca_topic_score_gemma":0.0004259555,"domain_scores_codex":[0.9996164,0.00009417986,0.0000195171,0.00006351269,0.0001850202,0.00002124943],"domain_scores_gemma":[0.999568,0.0001774833,0.00004761925,0.00009522792,0.00008979966,0.00002170751],"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":[0.0002302785,0.00009372792,0.0004086949,0.0004477922,0.00006535037,0.0003438644,0.0002993314,0.07618199,0.2343198,0.01727677,0.002726224,0.6676061],"study_design_scores_gemma":[0.00002979959,0.0001167079,0.0004730974,0.00003199774,0.00002911553,0.00103312,0.00003596842,0.8818246,0.09833439,0.005455095,0.01259568,0.00004050128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004247584,0.0002367295,0.9944068,0.00003379857,0.00001646072,0.00001635557,0.000007014854,0.0002344294,0.0008007769],"genre_scores_gemma":[0.0744371,0.0004610781,0.9226723,0.00005869326,0.00003525142,0.0000748505,0.00005690037,0.0001403171,0.002063524],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002249324,"threshold_uncertainty_score":0.007524788,"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."}}