{"id":"W2550025198","doi":"10.15353/vsnl.v1i1.39","title":"A Discretize-then-Optimize Approach to Super-Resolution Reconstruction and Motion Estimation","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Discretization; Resolution (logic); Iterative reconstruction; Motion (physics); Image (mathematics); Motion estimation; Algorithm; Image resolution; Inverse; Artificial intelligence; Mathematics; Computer vision; Process (computing); Computer science; Mathematical analysis; Geometry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0003833677,0.0001334313,0.0001221751,0.0002072259,0.0001163144,0.0002316734,0.0002599009,0.00005138764,7.325738e-7],"category_scores_gemma":[0.00014761,0.0001240064,0.00002291079,0.000353992,0.00006687681,0.001679436,0.0001756292,0.0001051088,0.00001354096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001126921,"about_ca_system_score_gemma":0.00001758291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000134296,"about_ca_topic_score_gemma":2.286497e-7,"domain_scores_codex":[0.9988309,0.00007627817,0.0001943944,0.0004427304,0.0002669186,0.0001888389],"domain_scores_gemma":[0.999335,0.00002305089,0.00007920343,0.0003388337,0.00008555307,0.0001383899],"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":[0.00003944283,0.00007240062,0.0001519664,0.00002572686,0.000005930284,0.000002412867,0.001830782,0.01073246,0.04927465,0.003160706,0.006463824,0.9282397],"study_design_scores_gemma":[0.0003045381,0.000077155,0.0003555976,0.00005260118,0.000004385505,0.00007586633,0.00003796025,0.9927762,0.001987638,0.003720191,0.0004098419,0.0001980787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01573929,0.00003564104,0.9768113,0.006271869,0.0001578822,0.0002387043,8.137976e-7,0.0004991147,0.0002453657],"genre_scores_gemma":[0.1512785,0.000003077211,0.8478096,0.0008114621,0.00003343989,0.00003043182,0.000005146484,0.000009870074,0.00001850071],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9820437,"threshold_uncertainty_score":0.5056832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01935551639781393,"score_gpt":0.2687290637137524,"score_spread":0.2493735473159385,"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."}}