{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009177759,0.00009673001,0.0001100513,0.00009058894,0.00008451504,0.0006387451,0.000157653,0.00002948575,0.00002033944],"category_scores_gemma":[0.00003991911,0.00007877181,0.00005895275,0.0001484853,0.00002958831,0.0002811959,0.00006158051,0.00005995409,0.00001961299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004030991,"about_ca_system_score_gemma":0.00008761996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001303308,"about_ca_topic_score_gemma":7.00097e-7,"domain_scores_codex":[0.9991295,0.00005632312,0.0001551243,0.0003073595,0.0001129829,0.0002387376],"domain_scores_gemma":[0.9994559,0.0002201725,0.00001587012,0.0002019313,0.00004187391,0.00006430871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008766181,0.00002170041,0.00000588041,0.0001659463,0.0000127951,0.00003882611,0.000404884,0.000008288689,0.3799776,0.02472177,0.001338896,0.5932947],"study_design_scores_gemma":[0.0002711774,0.0001083233,0.00006650386,0.00008038077,0.000009635099,0.00001153832,0.000009385973,0.2357194,0.7242963,0.01519001,0.02408399,0.0001533468],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002397942,0.001425248,0.9907948,0.001707416,0.0002407139,0.0001443255,7.588587e-7,0.0001687297,0.00312004],"genre_scores_gemma":[0.0741967,0.00002617164,0.923268,0.001076139,0.00006379071,0.000007980533,0.000001098341,0.000009403772,0.001350722],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5931413,"threshold_uncertainty_score":0.6159434,"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."}}