{"id":"W2051187242","doi":"10.1109/tip.2010.2052281","title":"Comments on \"Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering","year":2010,"lang":"en","type":"letter","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Illinois at Urbana-Champaign; Ryerson University","keywords":"Noise reduction; Discrete cosine transform; Wavelet transform; Image denoising; Noise (video); Computer science; Wavelet; Artificial intelligence; Pattern recognition (psychology); Signal-to-noise ratio (imaging); Algorithm; Image (mathematics); Mathematics","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","sts","scholarly_communication","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.000839719,0.001104075,0.0009307729,0.0007546448,0.001549258,0.002695701,0.001817709,0.001333465,0.00009524814],"category_scores_gemma":[0.00002228253,0.001108829,0.0003561219,0.001230842,0.0003590307,0.002482603,0.00001237981,0.00740657,0.0001680656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00031992,"about_ca_system_score_gemma":0.0004245428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003229458,"about_ca_topic_score_gemma":0.000009074108,"domain_scores_codex":[0.9944808,0.0005870024,0.000895365,0.001575191,0.001227529,0.001234154],"domain_scores_gemma":[0.9972448,0.0004663659,0.0004624042,0.00115133,0.0004558426,0.0002192235],"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":[0.0001864271,0.0004933964,1.173881e-7,0.0007352942,0.0001814995,0.001287798,0.003614902,0.0001629071,0.3620572,0.000005312808,0.3087569,0.3225182],"study_design_scores_gemma":[0.002496738,0.0004855413,7.63952e-7,0.001523098,0.0001744556,0.0001848871,0.0001148362,0.006288772,0.7484999,0.001284349,0.2368499,0.002096758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001076598,0.0001468482,0.8836814,0.1107742,0.001465754,0.0006524467,0.0001983037,0.0004993242,0.002474039],"genre_scores_gemma":[0.004820865,0.0000850699,0.6504273,0.3388852,0.001158601,0.0002958449,0.00009275042,0.0003902976,0.003844097],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3864428,"threshold_uncertainty_score":0.999963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01735826781127032,"score_gpt":0.281707967706056,"score_spread":0.2643496998947857,"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."}}