{"id":"W4387803699","doi":"10.1109/igarss52108.2023.10282110","title":"Poissonian Hyperspectral Image Denoising without Using Anscombe Transform","year":2023,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Henan University; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Hyperspectral imaging; Noise reduction; Artificial intelligence; Pattern recognition (psychology); Noise (video); Logarithm; Gaussian noise; Image denoising; Poisson distribution; Image (mathematics); Gaussian; Maximum a posteriori estimation; Mathematics; Computer science; Non-local means; Algorithm; Computer vision; Statistics; Maximum likelihood; Mathematical analysis; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007464272,0.0006603178,0.0006998409,0.0007054574,0.0004387826,0.0006580662,0.000929706,0.0008975672,0.0009001683],"category_scores_gemma":[0.001657787,0.0003146545,0.0008752808,0.0007103447,0.000766467,0.001448093,0.0009779917,0.000958505,0.0004778773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003119793,"about_ca_system_score_gemma":0.0006265703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001899361,"about_ca_topic_score_gemma":0.002829349,"domain_scores_codex":[0.9993433,0.00006224645,0.00003331139,0.0001678204,0.0003512894,0.00004189156],"domain_scores_gemma":[0.9994838,0.0001342858,0.00007192491,0.00008438645,0.0002040423,0.00002142939],"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":[0.0003724275,0.0001816883,0.004806363,0.0004663747,0.00019871,0.0006321069,0.0005560742,0.08702935,0.3708376,0.03902299,0.003623683,0.4922727],"study_design_scores_gemma":[0.00002191067,0.0001077167,0.00248969,0.0000179607,0.00006146594,0.0008462717,0.00009799343,0.8107519,0.1678772,0.00857144,0.009090179,0.00006634995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02249996,0.0002810518,0.9747049,0.0001194249,0.00005509482,0.00003535753,0.00003175652,0.000355517,0.001916886],"genre_scores_gemma":[0.3549375,0.0008595376,0.6357172,0.0003035077,0.0001055577,0.0001208475,0.0002577491,0.0002218655,0.007476321],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001899361,"threshold_uncertainty_score":0.003947556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03840786395568974,"score_gpt":0.3214883302746259,"score_spread":0.2830804663189361,"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."}}