{"id":"W2081057241","doi":"10.1109/icdsp.2009.5201145","title":"Storage-efficient quasi-Newton algorithms for image super-resolution","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Algorithm; Computer science; Broyden–Fletcher–Goldfarb–Shanno algorithm; Grayscale; Image resolution; Minification; Image (mathematics); Resolution (logic); Superresolution; Artificial intelligence; Computer vision","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":[0.0003081259,0.0001675624,0.0001557371,0.0001238226,0.0001977477,0.000209415,0.0007634933,0.00006042746,0.00000698829],"category_scores_gemma":[0.00009977882,0.0001509922,0.00007481562,0.0003236413,0.00004822284,0.0007566608,0.0001080979,0.0001019458,0.00002031834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001107258,"about_ca_system_score_gemma":0.00005830521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007372664,"about_ca_topic_score_gemma":7.637867e-7,"domain_scores_codex":[0.9986194,0.0000245512,0.0002234496,0.0004908445,0.0002472284,0.0003945725],"domain_scores_gemma":[0.9990293,0.00005222374,0.00007132506,0.0005424307,0.0002186553,0.00008611101],"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":[0.00002969956,0.001013441,0.000005041356,0.00004531089,0.000009525999,0.00002828312,0.0007299216,0.00057452,0.13027,0.1522078,0.03171557,0.6833709],"study_design_scores_gemma":[0.0002486976,0.0003380213,0.00006291118,0.00001720627,0.000003186534,0.0000171984,0.00001228649,0.9348371,0.03233375,0.02552866,0.006364107,0.0002368903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002163432,0.0001466561,0.9922605,0.004157388,0.0001374195,0.0003530729,0.000002423259,0.001479784,0.001246392],"genre_scores_gemma":[0.03225601,0.000005328543,0.9660583,0.0006730559,0.00006800835,0.0000357906,0.000003842513,0.00001055132,0.0008891367],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9342626,"threshold_uncertainty_score":0.6157284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02075905152196615,"score_gpt":0.3105352770627455,"score_spread":0.2897762255407793,"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."}}