{"id":"W3012290642","doi":"10.3390/app10051865","title":"Image Magnification Based on Bicubic Approximation with Edge as Constraint","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Bicubic interpolation; Mathematics; Polynomial; Magnification; Piecewise; Surface (topology); Image (mathematics); Image gradient; Enhanced Data Rates for GSM Evolution; Algorithm; Artificial intelligence; Computer vision; Edge detection; Computer science; Image processing; Linear interpolation; Geometry; Mathematical analysis","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":[0.0002325342,0.0005457386,0.0006069029,0.0006567937,0.000232678,0.0006660955,0.0007020563,0.0004340924,0.002200983],"category_scores_gemma":[0.001008683,0.0002660207,0.0005458453,0.0006709024,0.0003856961,0.0008509074,0.0006658892,0.0006251098,0.0005125981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004213712,"about_ca_system_score_gemma":0.0003319207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001719252,"about_ca_topic_score_gemma":0.001592499,"domain_scores_codex":[0.9995897,0.00003300577,0.00001554167,0.00006178187,0.0002678282,0.0000321197],"domain_scores_gemma":[0.9996411,0.00009779642,0.00003778768,0.00009969303,0.0001036089,0.00002008945],"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":[0.0002536111,0.0000678869,0.001479549,0.0003168536,0.00006381801,0.0002790671,0.0002388129,0.09598211,0.287459,0.01866469,0.002568926,0.5926257],"study_design_scores_gemma":[0.0000188258,0.0001416311,0.00123532,0.00001401795,0.00002856125,0.0007242195,0.00004717979,0.9209017,0.06604394,0.004119793,0.006692479,0.00003232107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02146073,0.0001973742,0.9760423,0.00004847238,0.00002796221,0.00002579608,0.00002339112,0.0004425032,0.001731505],"genre_scores_gemma":[0.3898426,0.0006088099,0.6041312,0.00008517307,0.00006351699,0.00007818664,0.0001859717,0.000214941,0.004789617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002200983,"threshold_uncertainty_score":0.007363021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02302052026084702,"score_gpt":0.265906706850001,"score_spread":0.2428861865891539,"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."}}