{"id":"W2990123081","doi":"10.1109/avss.2019.8909891","title":"Deep Single Image Enhancer","year":2019,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Convolutional neural network; High dynamic range; Image (mathematics); Pyramid (geometry); Terrain; Feature (linguistics); Range (aeronautics); Luminance; Dynamic range; Geography; Mathematics; Engineering","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":[0.0003357466,0.0009544138,0.0006356939,0.000547437,0.0002484851,0.0008425612,0.001609104,0.001004379,0.0186229],"category_scores_gemma":[0.0008058106,0.0002832678,0.0005879275,0.0005578897,0.000400344,0.001630713,0.001292703,0.001080863,0.004721859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005491886,"about_ca_system_score_gemma":0.0007299529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002268996,"about_ca_topic_score_gemma":0.004655362,"domain_scores_codex":[0.9997545,0.00001472942,0.000009009715,0.00006426215,0.00009495211,0.00006244863],"domain_scores_gemma":[0.9997967,0.00003374727,0.00002020325,0.00004929435,0.00008157813,0.00001852569],"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":[0.0003886513,0.0002069986,0.0006167178,0.0003487764,0.0001015604,0.000308968,0.00006784165,0.05745354,0.06931835,0.02426831,0.02769101,0.8192293],"study_design_scores_gemma":[0.00003232894,0.0001864254,0.0005397064,0.00004560038,0.00004133606,0.0003489003,0.00003815828,0.8922572,0.06096507,0.01370484,0.03181717,0.00002321528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02371927,0.001759052,0.9414193,0.0004208801,0.0003588395,0.0001583201,0.0009073169,0.006261101,0.02499598],"genre_scores_gemma":[0.4751869,0.001842008,0.4405085,0.0009233104,0.0001707637,0.0002289615,0.003799144,0.0008389518,0.07650147],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0186229,"threshold_uncertainty_score":0.06229985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006673486781840677,"score_gpt":0.2313750256117824,"score_spread":0.2247015388299417,"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."}}