{"id":"W4221142437","doi":"10.48550/arxiv.2202.00179","title":"Blind Image Deconvolution Using Variational Deep Image Prior","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Prior probability; Deconvolution; Artificial intelligence; Image (mathematics); Computer science; Blind deconvolution; Generalization; Maximum a posteriori estimation; Benchmark (surveying); Constraint (computer-aided design); Pattern recognition (psychology); Image restoration; Network architecture; A priori and a posteriori; Algorithm; Computer vision; Mathematics; Image processing; Maximum likelihood; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001634555,0.0002688519,0.0002171337,0.0002458083,0.0003126002,0.0001066465,0.0007114122,0.0001971287,0.0005850511],"category_scores_gemma":[0.00002396303,0.0003782623,0.0001478574,0.0004106723,0.00007998753,0.0003000655,0.0008466328,0.0006688241,0.00003846579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006220135,"about_ca_system_score_gemma":0.0001286764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005755978,"about_ca_topic_score_gemma":0.00000776348,"domain_scores_codex":[0.9988631,0.00003775345,0.0002091312,0.0005311897,0.00008227349,0.0002765277],"domain_scores_gemma":[0.9989182,0.00003548769,0.0001306669,0.0007035139,0.0001277801,0.0000843008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003447337,0.0001205403,0.000233038,0.0003511135,0.0001394328,0.00008686923,0.0001723661,0.9722937,0.006522067,0.01851726,0.0009137485,0.0006154291],"study_design_scores_gemma":[0.0002918855,0.000007116863,0.0002759878,0.0000293052,0.0001231805,0.000006208297,0.00004785958,0.967031,0.0005002653,0.03036203,0.0009187189,0.0004064349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09494829,0.00009651859,0.8997743,0.00002934719,0.0002144251,0.000337757,0.00007761789,0.0007980264,0.003723704],"genre_scores_gemma":[0.8849694,0.0001247376,0.1141659,0.00002359198,0.0001208941,0.000009790235,0.0001890221,0.00006984514,0.000326896],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7900211,"threshold_uncertainty_score":0.9998669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05173827587366066,"score_gpt":0.2111147455082858,"score_spread":0.1593764696346252,"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."}}