{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002749303,0.0008466726,0.0008013632,0.0006980933,0.0002376894,0.0005938162,0.0009722202,0.000579097,0.001326856],"category_scores_gemma":[0.0006523093,0.0002948922,0.000821845,0.0003061809,0.0004073311,0.0008212606,0.0006106843,0.0008567786,0.0004486247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003044344,"about_ca_system_score_gemma":0.0003322854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001007298,"about_ca_topic_score_gemma":0.001274467,"domain_scores_codex":[0.9997681,0.00001481893,0.000009745841,0.00005084346,0.0001285514,0.00002787727],"domain_scores_gemma":[0.9995988,0.0001011412,0.00008215145,0.00009532098,0.00009243595,0.00003018597],"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":[0.0003119745,0.0002074879,0.001185295,0.0005673441,0.0001391764,0.0005459727,0.0001886562,0.03851305,0.5692413,0.0016406,0.003498253,0.3839609],"study_design_scores_gemma":[0.00009006936,0.0005257456,0.003251716,0.0000466924,0.0001353163,0.001632701,0.00007575766,0.5298598,0.4502861,0.00245217,0.01158988,0.00005400027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07478643,0.001291579,0.9191749,0.0002323631,0.0001842004,0.0001394679,0.0001600327,0.002013244,0.002017846],"genre_scores_gemma":[0.4330432,0.001306313,0.5595271,0.0004183087,0.0002120827,0.00006996149,0.0004551131,0.0003015972,0.004666364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001326856,"threshold_uncertainty_score":0.004438758,"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."}}