{"id":"W2168561313","doi":"10.1109/tip.2005.846019","title":"Specification of the observation model for regularized image up-sampling","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Regularization (linguistics); Image resolution; Inverse problem; Computer vision; Artificial intelligence; Computer science; Fidelity; Image (mathematics); Sampling (signal processing); Mathematics; 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":[0.001615676,0.0004447084,0.0006311361,0.0002399489,0.0002323749,0.0006572965,0.0008701124,0.0007582429,0.0007580124],"category_scores_gemma":[0.00432445,0.0003285617,0.0005866555,0.0003352664,0.0009003673,0.001328127,0.0007783259,0.001427397,0.0003127279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005868126,"about_ca_system_score_gemma":0.0006302407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001137992,"about_ca_topic_score_gemma":0.0009235215,"domain_scores_codex":[0.9991943,0.0002644937,0.00004703324,0.0001639363,0.000287083,0.00004314786],"domain_scores_gemma":[0.9981425,0.0008248764,0.0002653729,0.0004457959,0.0002712264,0.00005038792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002547337,0.0001211833,0.002205801,0.0002573304,0.00006364049,0.0002674415,0.0003521989,0.632634,0.0910337,0.1539462,0.001572975,0.1172908],"study_design_scores_gemma":[0.000004344255,0.00001812421,0.0001439737,0.000004381489,0.000004000077,0.00006577429,0.000005539783,0.9893461,0.004929881,0.004932607,0.0005366931,0.000008594892],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002781542,0.00001681292,0.9969868,0.00002435873,0.00000397083,0.000008517774,0.000008437684,0.00005638881,0.0001131066],"genre_scores_gemma":[0.3326095,0.0001868968,0.6655412,0.00008480337,0.00003416309,0.0001356281,0.0001699992,0.0001106985,0.001127146],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001615676,"threshold_uncertainty_score":0.008544624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06069735515216133,"score_gpt":0.3180474375298847,"score_spread":0.2573500823777233,"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."}}