{"id":"W4313478437","doi":"10.1016/j.sigpro.2022.108926","title":"Rank minimization via adaptive hybrid norm for image restoration","year":2023,"lang":"en","type":"article","venue":"Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Singular value; Minification; Norm (philosophy); Matrix norm; Mathematical optimization; Rank (graph theory); Image restoration; Computer science; Mathematics; Algorithm; Image (mathematics); Singular value decomposition; Artificial intelligence; Image processing","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":[0.0006065478,0.0006301689,0.0006230607,0.0005214076,0.0002381949,0.0006771216,0.0006829248,0.000944094,0.002799915],"category_scores_gemma":[0.001716048,0.0002637508,0.0004796473,0.0006820094,0.0008888957,0.001028306,0.001185024,0.001315033,0.0007460912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002691687,"about_ca_system_score_gemma":0.000542285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001045532,"about_ca_topic_score_gemma":0.001712107,"domain_scores_codex":[0.9996274,0.0001382276,0.00001347142,0.00005403467,0.0001432495,0.00002366593],"domain_scores_gemma":[0.9995098,0.0002410299,0.00004841802,0.00007244135,0.0001066714,0.00002171368],"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":[0.0003816086,0.0001774457,0.0004158592,0.0004597896,0.0001305616,0.0001206978,0.000163694,0.3353632,0.05806554,0.2071609,0.01420538,0.3833553],"study_design_scores_gemma":[0.00001016409,0.00004934104,0.00008264786,0.00000780824,0.000008228479,0.00005686299,0.00001442943,0.9678693,0.004682573,0.02471809,0.002489113,0.00001162577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003508163,0.0001509999,0.9947644,0.0001298697,0.00003167319,0.000009382786,0.00003190222,0.00009763511,0.001275948],"genre_scores_gemma":[0.1550937,0.0006132316,0.8326477,0.0001827714,0.0001577305,0.000101231,0.000246023,0.0001983318,0.0107593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002799915,"threshold_uncertainty_score":0.009366691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02312188899811395,"score_gpt":0.249177246283302,"score_spread":0.2260553572851881,"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."}}