{"id":"W2804445799","doi":"10.1016/j.apm.2018.05.007","title":"A framelet algorithm for de-blurring images corrupted by multiplicative noise","year":2018,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Specialized Research Fund for the Doctoral Program of Higher Education of China; China Scholarship Council; Foundation for Distinguished Young Talents in Higher Education of Guangdong; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Algorithm; Regularization (linguistics); Multiplicative noise; Noise reduction; Minification; Multiplicative function; Noise (video); Speckle noise; Mathematics; Computer science; Sequence (biology); Speckle pattern; Artificial intelligence; Image (mathematics); Mathematical optimization","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":[0.0007038892,0.0002272981,0.0003381158,0.00006764875,0.0002599997,0.0002170186,0.0006448951,0.0001211569,0.00001134931],"category_scores_gemma":[0.00006640841,0.0002066073,0.00009583183,0.0002260049,0.0001408974,0.0001851945,0.0001314738,0.0001471619,0.00008639857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004722493,"about_ca_system_score_gemma":0.00003942602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003609448,"about_ca_topic_score_gemma":4.516637e-8,"domain_scores_codex":[0.9982395,0.00004097302,0.0003476741,0.0005291905,0.0002997453,0.0005429513],"domain_scores_gemma":[0.9981681,0.0008641377,0.0001076517,0.0005241816,0.0001740262,0.0001619335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006236161,0.000563348,7.576377e-7,0.0001586106,0.00008461077,0.000007545403,0.005199301,0.001758282,0.1506876,0.2160242,0.001753452,0.6236999],"study_design_scores_gemma":[0.0004616405,0.00005045636,1.881277e-7,0.00002378916,0.00001292968,0.000005521953,0.00002103157,0.6796172,0.1009268,0.218368,0.0003315826,0.0001808282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000473739,0.00006133007,0.9945959,0.0001268594,0.00005426221,0.0005495972,0.000008077937,0.0002635381,0.003866698],"genre_scores_gemma":[0.07108191,0.000005834706,0.9280552,0.0003391199,0.0001573251,0.000201483,0.000002904079,0.0000321687,0.0001240173],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6778589,"threshold_uncertainty_score":0.8425202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02849058167563439,"score_gpt":0.2939699402080008,"score_spread":0.2654793585323664,"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."}}