{"id":"W3173919805","doi":"","title":"Efficient Technique for Removal of White and Mixed Noises in Gray Scale Images","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bow Valley College","funders":"","keywords":"Artificial intelligence; Peak signal-to-noise ratio; Computer science; Image quality; Image processing; Grayscale; Pattern recognition (psychology); Entropy (arrow of time); Computer vision; Noise (video); Mean squared error; Noise reduction; Median filter; Image (mathematics); Image noise; Mathematics; Statistics","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":[0.0005090752,0.0006007834,0.000714401,0.001403319,0.0003324559,0.00072631,0.0006490687,0.0007292058,0.001975296],"category_scores_gemma":[0.00113574,0.0002389843,0.0007759169,0.001207309,0.0004590017,0.001110421,0.0005681612,0.0008096572,0.00128633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002366792,"about_ca_system_score_gemma":0.0004720472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006115854,"about_ca_topic_score_gemma":0.001084947,"domain_scores_codex":[0.9994919,0.00004677033,0.00002956448,0.0000734774,0.0003273228,0.00003090965],"domain_scores_gemma":[0.99957,0.0001190327,0.00006200541,0.00007012385,0.0001651083,0.00001379299],"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":[0.0002187927,0.0001006298,0.0008920117,0.0007737669,0.00009265405,0.0005365529,0.0002896754,0.008660185,0.37921,0.007585465,0.003380616,0.5982596],"study_design_scores_gemma":[0.00005906171,0.0009792341,0.008344151,0.0002958932,0.0003264229,0.005304692,0.0003967782,0.2605993,0.6432672,0.01328638,0.06702183,0.000119043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02545275,0.002980132,0.9670346,0.0002234204,0.0001371619,0.00007241219,0.00008316102,0.000795896,0.003220515],"genre_scores_gemma":[0.2134332,0.006010272,0.7685207,0.0002638282,0.0001701723,0.0001205173,0.0003760043,0.0002034184,0.01090198],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001975296,"threshold_uncertainty_score":0.006608009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00640377086958605,"score_gpt":0.2526869275186281,"score_spread":0.246283156649042,"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."}}