{"id":"W1966663921","doi":"10.1109/tip.2011.2168415","title":"Wavelet-Domain Blur Invariants for Image Analysis","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Deblurring; Wavelet; Invariant (physics); Artificial intelligence; Computer vision; Domain (mathematical analysis); Computer science; Fourier transform; Image restoration; Frequency domain; Mathematics; Image (mathematics); Pattern recognition (psychology); Image processing; Mathematical analysis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008137875,0.000269083,0.0003595263,0.0006218266,0.0006229137,0.0004911487,0.0007638866,0.00009865747,0.0000865676],"category_scores_gemma":[0.00002070462,0.0002573068,0.0003294174,0.001665687,0.000114304,0.001817756,0.000005211972,0.0002567095,0.00005081783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005816014,"about_ca_system_score_gemma":0.0001401128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003947678,"about_ca_topic_score_gemma":0.00001225822,"domain_scores_codex":[0.9979857,0.0001503006,0.0004054667,0.0006613546,0.0002990208,0.0004981158],"domain_scores_gemma":[0.9986916,0.0001331263,0.0001542541,0.0005760245,0.0002993301,0.0001457151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002812407,0.0008941343,0.000006727424,0.0002379243,0.0006031237,0.0001665719,0.0092836,0.0002149959,0.1806609,0.000505647,0.0002737948,0.8068713],"study_design_scores_gemma":[0.00212057,0.0002821183,0.0002299482,0.00009760061,0.0007014163,0.00007021024,0.000234008,0.2332617,0.7460732,0.01570612,0.0002999181,0.0009231585],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009403155,0.00004671529,0.995388,0.0001817986,0.000272955,0.0002475144,0.00001401773,0.0002750941,0.002633621],"genre_scores_gemma":[0.2747652,0.00000455707,0.7243373,0.0003162092,0.00003474655,0.00006146226,0.000001353363,0.00002446047,0.0004546096],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8059481,"threshold_uncertainty_score":0.9999879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04078650459459445,"score_gpt":0.2914927199401173,"score_spread":0.2507062153455228,"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."}}