{"id":"W4386076237","doi":"10.1109/cvpr52729.2023.01752","title":"GamutMLP: A Lightweight MLP for Color Loss Recovery","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gamut; Computer science; Artificial intelligence; RGB color model; Color space; Computer vision; RGB color space; Lightness; Color depth; Color balance; Color constancy; Clipping (morphology); Color management; Computer graphics (images); Color image; Image processing; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006356085,0.002132342,0.0007779291,0.0008547556,0.0003655186,0.000931715,0.003119203,0.001142117,0.005502438],"category_scores_gemma":[0.002539415,0.0006251176,0.0009598717,0.0008146829,0.0004670539,0.001769179,0.001327718,0.00221248,0.002888999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001027831,"about_ca_system_score_gemma":0.0008327522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008842263,"about_ca_topic_score_gemma":0.01606873,"domain_scores_codex":[0.9996736,0.00004115301,0.00001346098,0.0001081915,0.0001102688,0.00005339804],"domain_scores_gemma":[0.999576,0.0001080148,0.00002999068,0.0001571957,0.0001062458,0.00002238517],"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":[0.0006424887,0.0003502043,0.002669497,0.0004437369,0.0002263995,0.0002829219,0.00007243612,0.2533659,0.0295706,0.002672427,0.04867886,0.6610247],"study_design_scores_gemma":[0.00003079631,0.00006489102,0.0007111429,0.00002177166,0.00002076769,0.00008660585,0.00001414376,0.9750277,0.01688815,0.001910698,0.005202284,0.00002097746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1216663,0.001608898,0.7705094,0.0005675961,0.0004555972,0.0003956682,0.008200957,0.08708908,0.009506452],"genre_scores_gemma":[0.3771719,0.0007910965,0.5804234,0.0005831469,0.0000787649,0.0006208307,0.02148786,0.003188484,0.01565453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008842263,"threshold_uncertainty_score":0.01840746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0182752534558556,"score_gpt":0.2757420893765767,"score_spread":0.2574668359207211,"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."}}