{"id":"W4404954273","doi":"10.1109/icares64249.2024.10768091","title":"Joint Image De-Noising and Enhancement for Satellite-Based SAR","year":2024,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Joint (building); Computer science; Synthetic aperture radar; Satellite; Remote sensing; Computer vision; Artificial intelligence; Geology; Engineering; Aerospace engineering","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":[0.000616074,0.0003603268,0.0003471942,0.0003787388,0.0001217913,0.0003138946,0.0002626775,0.0003185709,0.0004624841],"category_scores_gemma":[0.0008244346,0.0001728168,0.0003208931,0.0002908671,0.000349965,0.0004797152,0.0004503574,0.0002795681,0.00019962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009553154,"about_ca_system_score_gemma":0.0002401123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005033601,"about_ca_topic_score_gemma":0.001336428,"domain_scores_codex":[0.9997806,0.00004852628,0.00001343822,0.00003704233,0.0001019622,0.00001841653],"domain_scores_gemma":[0.9997088,0.0001045804,0.00005429488,0.00005103826,0.00006635177,0.00001479684],"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":[0.0004726324,0.0001838324,0.002207655,0.0002141234,0.00009634733,0.0001624162,0.0001508012,0.06942213,0.5250708,0.00245072,0.000473579,0.3990949],"study_design_scores_gemma":[0.00002148951,0.0003208591,0.005900774,0.00001019482,0.00005222506,0.00042049,0.00005586822,0.7410598,0.2475001,0.0010583,0.003573536,0.00002621597],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1484426,0.0007694039,0.8491511,0.00007553442,0.00003509669,0.00003487888,0.00002386546,0.0003192594,0.001148175],"genre_scores_gemma":[0.498296,0.0006817842,0.4976238,0.000035454,0.00003411451,0.0000261476,0.0001033668,0.00007127006,0.003128157],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.000616074,"threshold_uncertainty_score":0.003258169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03102073132875951,"score_gpt":0.3072569400849725,"score_spread":0.276236208756213,"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."}}