{"id":"W2166011696","doi":"10.1109/crv.2007.40","title":"Images Restoration Using an Iterative Dynamic Programming Approach","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Grayscale; Streak; Image restoration; Computer science; Dynamic programming; Impulse noise; Artificial intelligence; Noise (video); Computer vision; Minification; Gaussian noise; Iterative method; Impulse (physics); Algorithm; Mathematical optimization; Image (mathematics); Pixel; Image processing; Mathematics","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":[0.0004705027,0.0008568607,0.001046579,0.0007958922,0.0004556795,0.0009066297,0.001042552,0.001133191,0.001846559],"category_scores_gemma":[0.000967056,0.0005088118,0.0008834763,0.0006616495,0.0005900861,0.001076618,0.001076377,0.001521135,0.0006149538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004077564,"about_ca_system_score_gemma":0.0008599923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001214654,"about_ca_topic_score_gemma":0.00131454,"domain_scores_codex":[0.9995258,0.00008413639,0.00001970978,0.00009482272,0.0002443266,0.00003123446],"domain_scores_gemma":[0.9997419,0.0001033544,0.00003675954,0.00002641982,0.00007636763,0.00001521568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006970218,0.00009975526,0.0002166447,0.0002661107,0.00009616755,0.0001875614,0.000138096,0.5221419,0.04079152,0.04190887,0.002787082,0.3912966],"study_design_scores_gemma":[0.000009896394,0.00003241847,0.00005627334,0.00001091942,0.00001348894,0.000142102,0.000009398804,0.9821334,0.005870358,0.006286756,0.005417543,0.00001739885],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005182772,0.00006168988,0.9987527,0.00003175626,0.00001315208,0.000008214977,0.000002954153,0.00008453328,0.0005267143],"genre_scores_gemma":[0.0371688,0.0004174476,0.9587635,0.00006714837,0.00005210757,0.0001095563,0.00003689304,0.0001180058,0.003266537],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001846559,"threshold_uncertainty_score":0.006177306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03086780617327794,"score_gpt":0.3443093920190218,"score_spread":0.3134415858457438,"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."}}