{"id":"W4234833824","doi":"10.32920/ryerson.14653488","title":"Human visual system inspired saliency guided edge preserving tone-mapping for high dynamic range imaging","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Tone mapping; Artificial intelligence; Human visual system model; Computer vision; Computer science; High dynamic range; High-dynamic-range imaging; Enhanced Data Rates for GSM Evolution; Filter (signal processing); Pixel; Tone (literature); Bilateral filter; Dynamic range; Naturalness; Image (mathematics); Physics","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":[0.0001392276,0.0003018162,0.0001897111,0.0002590718,0.0001113522,0.0003020983,0.0002738854,0.0002464398,0.001569592],"category_scores_gemma":[0.0003628591,0.0001014763,0.0002931604,0.0001572573,0.0002151204,0.000385066,0.0002650023,0.0002803227,0.0002724732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001280597,"about_ca_system_score_gemma":0.0001439311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003368197,"about_ca_topic_score_gemma":0.0005364977,"domain_scores_codex":[0.9999427,0.00001205587,0.000001939197,0.0000123082,0.00002359327,0.000007515247],"domain_scores_gemma":[0.9998931,0.00003607336,0.00001357455,0.00001978105,0.00002904343,0.000008405882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002067019,0.00005266955,0.0004464377,0.0002386577,0.00005336501,0.0001958241,0.0001384546,0.02346701,0.7169026,0.004981275,0.0009654957,0.2523516],"study_design_scores_gemma":[0.0000357688,0.0004579573,0.003431326,0.00002248689,0.00007637384,0.001129361,0.0000694913,0.6448015,0.3347478,0.006331806,0.008862791,0.00003341149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1149576,0.0009287235,0.879685,0.00010354,0.00006114984,0.0000558315,0.00003426919,0.0005243346,0.003649572],"genre_scores_gemma":[0.6921193,0.0007863832,0.3025223,0.00008253412,0.00005849365,0.00003593692,0.00006329861,0.00008198342,0.004249795],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001569592,"threshold_uncertainty_score":0.005250812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0263237841040121,"score_gpt":0.3324057861757245,"score_spread":0.3060820020717124,"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."}}