{"id":"W4400010660","doi":"10.37493/2307-910x.2022.3.3","title":"Cleaning images from impulse noise in a binary symmetrical channel","year":2022,"lang":"en","type":"article","venue":"Sovremennaya nauka i innovatsii","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Higher Education of the Russian Federation; Centre de Recherches Mathématiques","keywords":"Impulse noise; Pixel; Impulse (physics); Binary number; Computer science; Noise (video); Median filter; Dark-frame subtraction; Binary image; Salt-and-pepper noise; Channel (broadcasting); Computer vision; Gaussian noise; Image noise; Artificial intelligence; Mathematics; Image (mathematics); Image processing; Physics; Telecommunications; Arithmetic","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005447483,0.0004356752,0.0008506565,0.0008843165,0.0003799941,0.0007286963,0.0005836355,0.0008903077,0.0008767379],"category_scores_gemma":[0.002047632,0.000272815,0.0005597509,0.0006388985,0.0007529308,0.001099253,0.000790338,0.0007980646,0.0004635032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002551224,"about_ca_system_score_gemma":0.0003324578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000517235,"about_ca_topic_score_gemma":0.0006423646,"domain_scores_codex":[0.9994469,0.00007004612,0.00002793931,0.0001174655,0.0002817189,0.00005578505],"domain_scores_gemma":[0.9991702,0.0002828273,0.0001211595,0.0001823413,0.0002021147,0.00004135002],"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":[0.0007911188,0.00009833865,0.002018404,0.0006174486,0.0001469193,0.000940553,0.000374215,0.04272318,0.4527606,0.01465068,0.002050029,0.4828285],"study_design_scores_gemma":[0.00003563974,0.0003058935,0.003792001,0.00006220093,0.000143979,0.002376195,0.0002127476,0.4137884,0.559742,0.01021943,0.009207017,0.0001143305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07479697,0.0005174148,0.9224995,0.000176967,0.0001366272,0.0000294014,0.00005357613,0.0003962706,0.001393312],"genre_scores_gemma":[0.4236661,0.0008352398,0.5692206,0.0001871063,0.000139484,0.00004506926,0.0002032934,0.000169286,0.005533847],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008903077,"threshold_uncertainty_score":0.002932966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02693478287711493,"score_gpt":0.2761714112093198,"score_spread":0.2492366283322049,"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."}}