{"id":"W2023963773","doi":"10.1109/cvprw.2012.6239193","title":"Gradient domain color restoration of clipped highlights","year":2012,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hue; Clipping (morphology); Artificial intelligence; Colored; Computer vision; Color space; Color correction; Computer science; Lightness; Boundary (topology); Color balance; Mathematics; Color image; Image (mathematics); Image processing; Mathematical analysis; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000330257,0.00006134907,0.0000810549,0.00007003424,0.00003593985,0.00001981219,0.0003090857,0.00002875885,0.00002341511],"category_scores_gemma":[0.00001060719,0.0000491895,0.00002441398,0.0001795632,0.0000252321,0.0007075924,0.0001032237,0.0000294856,0.00004296268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004079078,"about_ca_system_score_gemma":0.00001387399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000156825,"about_ca_topic_score_gemma":0.000005840899,"domain_scores_codex":[0.9993155,0.00004437504,0.0001673169,0.0001124255,0.0001821142,0.0001782471],"domain_scores_gemma":[0.9994687,0.00002351204,0.00007675971,0.0003360752,0.00005006379,0.00004482109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003432832,0.0001106778,0.0003626256,0.000006177188,0.000004114169,6.339855e-7,0.0006357233,3.726541e-7,0.06200172,0.9252979,0.009596653,0.001980033],"study_design_scores_gemma":[0.0001745025,0.0001667553,0.003673911,0.00001057595,0.00000273337,0.000002733171,0.00001609718,0.0008158205,0.9523475,0.005544401,0.03710712,0.0001378531],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09541371,0.00003666489,0.8915394,0.0006075658,0.0002952936,0.0001651771,2.496785e-7,0.0002510508,0.01169088],"genre_scores_gemma":[0.7142558,0.000004781666,0.2851085,0.00009662397,0.00003592775,0.00001468108,8.960522e-7,0.000002499971,0.0004803515],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9197534,"threshold_uncertainty_score":0.200589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01538371212332049,"score_gpt":0.2586726408576112,"score_spread":0.2432889287342907,"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."}}