{"id":"W2771946679","doi":"10.1007/s11760-017-1215-3","title":"Sparsity-based no-reference image quality assessment for automatic denoising","year":2017,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer science; Ground truth; Orientation (vector space); Noise reduction; Noise (video); Image (mathematics); Non-local means; Computer vision; Image quality; Measure (data warehouse); Pattern recognition (psychology); Video denoising; Quality (philosophy); Mathematics; Image denoising; Data mining; Video processing; Video tracking","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":[0.00198856,0.0006962682,0.0006871737,0.001182986,0.0003200687,0.0009033078,0.000760618,0.001150913,0.001768231],"category_scores_gemma":[0.005640103,0.0003220531,0.0005924869,0.0006211397,0.0006766217,0.001331581,0.001059558,0.0008405244,0.00076988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000307052,"about_ca_system_score_gemma":0.0005959109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009461142,"about_ca_topic_score_gemma":0.001963713,"domain_scores_codex":[0.9990796,0.0002104823,0.00006062515,0.0001622113,0.00042345,0.00006364322],"domain_scores_gemma":[0.9975433,0.0007290808,0.0002463893,0.0004046645,0.000988099,0.00008847111],"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":[0.00158923,0.0002842652,0.004420243,0.0007056461,0.0002312281,0.0002368111,0.0002281055,0.03955062,0.3645239,0.009596274,0.003257922,0.5753759],"study_design_scores_gemma":[0.00004847897,0.0003541111,0.009774646,0.00008790714,0.0002287297,0.0009416303,0.00008366882,0.7793812,0.198991,0.006192444,0.003836484,0.0000795893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03695473,0.00047487,0.9602712,0.0001182513,0.00004024172,0.00004435586,0.0001028038,0.0004011166,0.001592328],"genre_scores_gemma":[0.4459294,0.0009786226,0.548833,0.0001439639,0.0001036815,0.00008144757,0.0006149577,0.0003193305,0.002995567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00198856,"threshold_uncertainty_score":0.01051664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07620575415809538,"score_gpt":0.3917533244550718,"score_spread":0.3155475702969764,"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."}}