{"id":"W1719460476","doi":"10.1109/tip.2014.2332755","title":"High-Accuracy Total Variation With Application to Compressed Video Sensing","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"ENCODE; Redundancy (engineering); Compressed sensing; Minification; Computational complexity theory; Quadratic equation; Image restoration; Iterative reconstruction; Total variation denoising; Quadratic programming","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":[0.00008500451,0.0002119382,0.0001884669,0.000173729,0.0002302874,0.00017363,0.00009307882,0.00007026835,0.000007508013],"category_scores_gemma":[0.00000649208,0.0002094673,0.00003481683,0.0003401331,0.00003072435,0.0003789759,0.000001130334,0.0002124004,0.00003264495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007011345,"about_ca_system_score_gemma":0.00001702597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005590491,"about_ca_topic_score_gemma":0.00001338113,"domain_scores_codex":[0.9990507,0.00002887468,0.0002101127,0.000281226,0.0001950661,0.0002339951],"domain_scores_gemma":[0.9993627,0.00007306782,0.00005405146,0.0002857504,0.0001400093,0.0000844124],"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":[0.0000366774,0.00002783046,4.454899e-7,0.00003684563,0.00001605714,0.000001447263,0.0002583122,0.3392385,0.4056852,0.00001068026,0.00005764783,0.2546304],"study_design_scores_gemma":[0.0002184183,0.00005344634,0.00009887476,0.0001497148,0.0000332012,0.00001902848,0.00001783949,0.5430505,0.4557941,0.0001468539,0.0001858022,0.0002321912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04434312,0.00001140155,0.953343,0.0001103285,0.000137081,0.0002634612,0.000003915708,0.00134359,0.0004441136],"genre_scores_gemma":[0.8726719,0.000002619721,0.1270203,0.000116355,0.00008152976,0.00002960584,0.000003217914,0.00005752082,0.00001695011],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8283288,"threshold_uncertainty_score":0.8541829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006075626871021833,"score_gpt":0.2179854138974486,"score_spread":0.2119097870264268,"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."}}