{"id":"W3011905647","doi":"10.1109/access.2020.2981641","title":"Raindrop Removal With Light Field Image Using Image Inpainting","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Shaanxi Province","keywords":"Inpainting; Artificial intelligence; Computer vision; Binary image; Computer science; Image (mathematics); Image restoration; Light field; Field (mathematics); Feature detection (computer vision); Image quality; Filter (signal processing); Image processing; Mathematics","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":[0.0001229775,0.0001547842,0.0001743198,0.0000651295,0.0001785371,0.0006810621,0.00118488,0.00003112403,0.00003306625],"category_scores_gemma":[0.0001136054,0.0001285366,0.0000463599,0.0005926433,0.00003284547,0.002939436,0.0003904612,0.0002277097,0.00003629435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002071554,"about_ca_system_score_gemma":0.00004630402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002269018,"about_ca_topic_score_gemma":0.000001517279,"domain_scores_codex":[0.9987333,0.00004244332,0.0001997986,0.0004494556,0.0002573107,0.0003176809],"domain_scores_gemma":[0.9991996,0.00007153151,0.0001178213,0.0003660521,0.00009767302,0.0001473782],"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":[0.0001014948,0.0001006872,0.001733786,0.0001797756,0.0000561046,0.002999481,0.004979698,0.0007616356,0.7153975,0.001381042,0.01143564,0.2608731],"study_design_scores_gemma":[0.0006676795,0.00009162968,0.0001226462,0.0001159327,0.000009435873,0.0001581206,0.00006374615,0.5805795,0.4071035,0.0005944953,0.01003043,0.0004629275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.028304,0.00002999478,0.9601997,0.006626482,0.0002142274,0.0001046776,4.540815e-7,0.0002242615,0.004296185],"genre_scores_gemma":[0.4367736,0.000004787146,0.5538793,0.008994347,0.0002687915,0.000002515942,4.123299e-7,0.00002271961,0.00005355579],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5798178,"threshold_uncertainty_score":0.6567498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03552908378186256,"score_gpt":0.3273393212182252,"score_spread":0.2918102374363626,"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."}}