{"id":"W4206238523","doi":"10.1007/s10915-021-01721-7","title":"Image Multiplicative Denoising Using Adaptive Euler’s Elastica as the Regularization","year":2022,"lang":"en","type":"article","venue":"Journal of Scientific Computing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Mathematics; Augmented Lagrangian method; Regularization (linguistics); Backward Euler method; Noise reduction; Multiplicative noise; Algorithm; Multiplicative function; Euler's formula; Gaussian; Total variation denoising; Applied mathematics; Nonlinear system; Noise (video); Euler equations; Image (mathematics); Mathematical analysis; Computer science; Artificial intelligence; Transmission (telecommunications)","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.006001693,0.0001422499,0.0002259617,0.0003546725,0.002717005,0.001032955,0.001611359,0.00002374642,0.00002245406],"category_scores_gemma":[0.0005745318,0.0001099122,0.0001649064,0.00162908,0.000226735,0.0007284,0.001044398,0.0005617376,0.000006801901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002378871,"about_ca_system_score_gemma":0.0003744858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000946929,"about_ca_topic_score_gemma":2.566251e-7,"domain_scores_codex":[0.9968101,0.0008303454,0.00060868,0.0003579291,0.001058862,0.0003340639],"domain_scores_gemma":[0.9969546,0.0008391028,0.0009566034,0.0004454948,0.0007072301,0.00009700823],"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":[0.0001494862,0.0003245099,0.0001206929,0.00002074223,0.0001529515,0.000392122,0.02547093,0.1334149,0.6754618,0.01733075,0.001781086,0.14538],"study_design_scores_gemma":[0.0004984338,0.0001480032,0.0003557355,0.00005930924,0.00003209816,0.001110879,0.0009392037,0.9798023,0.00860071,0.007023822,0.001257337,0.000172238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1075481,0.0001667221,0.8890383,0.0005234373,0.002261572,0.0001129738,0.000001080034,0.00002756003,0.0003202207],"genre_scores_gemma":[0.6386733,4.618774e-7,0.3608701,0.000126472,0.0001715538,5.367893e-7,4.707768e-7,0.00001011287,0.0001469233],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8463873,"threshold_uncertainty_score":0.9985813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03667957919587975,"score_gpt":0.3005816841508532,"score_spread":0.2639021049549734,"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."}}