{"id":"W1606433989","doi":"10.1109/icassp.2015.7178172","title":"Hyper-spectral impulse denoising: A row-sparse Blind Compressed Sensing formulation","year":2015,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Noise reduction; Compressed sensing; Computer science; Impulse (physics); Artificial intelligence; Pattern recognition (psychology); Impulse noise; Sparse approximation; Gaussian noise; Noise measurement; Gaussian; Fidelity; Video denoising; Algorithm; Pixel","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00103821,0.0006060642,0.0007080882,0.00051135,0.0001965547,0.0008891505,0.000942219,0.001543938,0.001826273],"category_scores_gemma":[0.001791475,0.000305646,0.000491656,0.0007260522,0.001099859,0.001070411,0.001009741,0.001474059,0.0006614661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003967758,"about_ca_system_score_gemma":0.0009169098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001124961,"about_ca_topic_score_gemma":0.001440381,"domain_scores_codex":[0.9995477,0.000145034,0.00002077097,0.00009360223,0.000163564,0.00002932024],"domain_scores_gemma":[0.9993957,0.0003321012,0.00006549384,0.00006382506,0.0001122653,0.00003058011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002406771,0.0001683419,0.0007165492,0.0007519601,0.0001201551,0.0002713698,0.0002836752,0.5746267,0.03928333,0.1274261,0.008753357,0.2473579],"study_design_scores_gemma":[0.00001471229,0.00006013837,0.0001287516,0.00002553245,0.00001531842,0.0001654623,0.00002913064,0.9729953,0.00457739,0.01838566,0.003586676,0.00001591822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002134927,0.000207573,0.9963499,0.0002125885,0.0000273975,0.00001741544,0.00004803219,0.00006519481,0.000937007],"genre_scores_gemma":[0.1565457,0.00176116,0.8327383,0.0005737679,0.000399544,0.0001927217,0.000474343,0.0001449494,0.00716958],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001826273,"threshold_uncertainty_score":0.006109536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07924011143410371,"score_gpt":0.3113151045176591,"score_spread":0.2320749930835554,"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."}}