{"id":"W4310352586","doi":"10.21203/rs.3.rs-2310001/v1","title":"ICRICS: Iterative Compensation Recovery for Image Compressive Sensing","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Compressed sensing; Computer science; Compensation (psychology); Noise (video); Similarity (geometry); Image (mathematics); Algorithm; Sampling (signal processing); SIGNAL (programming language); Peak signal-to-noise ratio; Control theory (sociology); Artificial intelligence; Computer vision; Control (management)","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":[0.0005941237,0.000701567,0.0004584451,0.0006288688,0.0003230577,0.0004460993,0.0008088567,0.0007337829,0.002254847],"category_scores_gemma":[0.001231503,0.0001950419,0.0003294195,0.0005170509,0.0007575416,0.0005649985,0.0008411999,0.0007520334,0.0005880526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003823593,"about_ca_system_score_gemma":0.0008687078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001636021,"about_ca_topic_score_gemma":0.002004232,"domain_scores_codex":[0.9993586,0.0001140894,0.00002766324,0.0001104266,0.0003447032,0.00004464895],"domain_scores_gemma":[0.9996308,0.00009686996,0.00005841385,0.00006814789,0.0001248041,0.00002103886],"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":[0.0004675699,0.0001524093,0.0008319484,0.0003476299,0.00008588906,0.0002654088,0.0001745571,0.1667446,0.1717244,0.03107327,0.01124086,0.6168914],"study_design_scores_gemma":[0.000024047,0.000136926,0.0003053783,0.0000170985,0.000009575348,0.0001773746,0.00001414646,0.9580434,0.03312093,0.002421107,0.005709915,0.00002010509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009512413,0.0003816376,0.9862692,0.0001635433,0.0001100568,0.00007963365,0.00004870503,0.001024265,0.002410581],"genre_scores_gemma":[0.3771705,0.0004506002,0.6137952,0.0002592374,0.0001764729,0.00017674,0.0002712765,0.0001089757,0.007591189],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002254847,"threshold_uncertainty_score":0.007543206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07868981833216729,"score_gpt":0.3829435355090559,"score_spread":0.3042537171768886,"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."}}