{"id":"W3137932859","doi":"10.1109/tcsvt.2021.3066523","title":"Gaussian-Wiener Representation and Hierarchical Coding Scheme for Focal Stack Images","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Wiener deconvolution; Algorithm; Gaussian; Computer science; Encoder; Coding (social sciences); Deconvolution; Mathematics; Wiener filter; Computer vision; Artificial intelligence; Blind deconvolution; Statistics","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":[0.0001060581,0.000139901,0.0002250036,0.0002049511,0.0002628906,0.00009279222,0.00007258231,0.0002378586,0.000002447538],"category_scores_gemma":[0.00001729716,0.0001435156,0.00005201205,0.0002551209,0.00009980929,0.0001146966,0.000001908305,0.0002273749,8.571926e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002590704,"about_ca_system_score_gemma":0.00002179758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004703628,"about_ca_topic_score_gemma":0.000005783041,"domain_scores_codex":[0.9991198,0.00001069639,0.0002533941,0.0003194888,0.00006687621,0.0002297241],"domain_scores_gemma":[0.999465,0.0001238177,0.00003541819,0.0002174594,0.0001061717,0.00005216203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002937477,0.0001688277,0.00006279402,0.001945227,0.0002348519,0.00001342069,0.0002226387,0.001752644,0.57174,0.05559735,0.003481151,0.3647518],"study_design_scores_gemma":[0.001739074,0.00027057,0.0000346961,0.0003645358,0.0001431707,0.0004930761,0.0006769357,0.1938531,0.7478369,0.01173371,0.04218161,0.0006726141],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007609923,0.0009498216,0.9889766,0.0008461606,0.0001936561,0.0005580558,0.0001256251,0.0006006112,0.0001395623],"genre_scores_gemma":[0.9897638,0.0003155952,0.008448238,0.00003475174,0.00003984323,0.001132552,0.000009278092,0.00003847773,0.0002174471],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9821539,"threshold_uncertainty_score":0.5852395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02646236088930881,"score_gpt":0.2820415991536359,"score_spread":0.2555792382643271,"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."}}