{"id":"W1595923625","doi":"10.1007/978-3-642-03641-5_9","title":"A PDE Approach to Coupled Super-Resolution with Non-parametric Motion","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Regularization (linguistics); Parametric statistics; Affine transformation; Motion (physics); Computer science; Motion estimation; Algorithm; Computer vision; Resolution (logic); Artificial intelligence; Superresolution; Mathematics; Image (mathematics); Mathematical optimization; Geometry","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":[0.0007965923,0.0006709903,0.0006119058,0.002036267,0.00031421,0.0007734188,0.003718514,0.0003155064,0.000001817391],"category_scores_gemma":[0.0001366448,0.0005787686,0.00008760166,0.002635522,0.0004665601,0.001277871,0.0009184766,0.0008895381,0.0000234941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006422644,"about_ca_system_score_gemma":0.0004800783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002059372,"about_ca_topic_score_gemma":0.00001120004,"domain_scores_codex":[0.9950956,0.00002660799,0.0004884556,0.002191712,0.001355171,0.0008425071],"domain_scores_gemma":[0.9970909,0.000170206,0.0002828403,0.001697713,0.0005054983,0.0002528138],"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":[0.00001701216,0.00009807717,0.0000136805,0.00004787177,0.000006276522,0.00003716179,0.0004357092,0.08015122,0.0005940736,0.003827788,0.0000231193,0.914748],"study_design_scores_gemma":[0.0002310838,0.0004384359,0.0001218901,0.0003534081,0.000008121146,0.00008791276,9.389213e-8,0.9323446,0.001491902,0.06384834,0.0003282807,0.0007460035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003210762,0.0002529795,0.9944228,0.0004744668,0.0002672538,0.0008507139,0.000001649736,0.0006006243,0.003097388],"genre_scores_gemma":[0.05221512,0.00001830632,0.9459843,0.001331556,0.0001788788,0.00003135435,0.000005965268,0.00004003111,0.0001944856],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.914002,"threshold_uncertainty_score":0.9996664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01495345317696465,"score_gpt":0.2488115079561784,"score_spread":0.2338580547792137,"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."}}