{"id":"W4400443154","doi":"10.2139/ssrn.4890197","title":"Slrcnn: Integrating Local Sparse Low-Rank and Cnn Denoiser for Hyperspectral and Multispectral Image Fusion","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multispectral image; Hyperspectral imaging; Rank (graph theory); Artificial intelligence; Computer science; Image fusion; Pattern recognition (psychology); Image (mathematics); Computer vision; Remote sensing; Mathematics; Geology","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0008289927,0.0006289449,0.0005788398,0.0003368691,0.0002028542,0.0003069528,0.0002982134,0.0003830889,0.0000220915],"category_scores_gemma":[0.00009045758,0.0005837272,0.0002271433,0.0001201112,0.0001648292,0.000218814,0.0004292986,0.006691693,0.000007455008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001416133,"about_ca_system_score_gemma":0.0005354096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003814221,"about_ca_topic_score_gemma":0.0003438025,"domain_scores_codex":[0.9963776,0.00004407925,0.0005729389,0.0005984292,0.0002500378,0.002156899],"domain_scores_gemma":[0.999198,0.00009086439,0.0001293688,0.0002759816,0.0001160677,0.000189779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004515116,0.0001563214,0.0001377859,0.002993702,0.001330394,0.0002557279,0.002423178,0.003444306,0.3474539,0.0484931,0.002025745,0.5908344],"study_design_scores_gemma":[0.001510114,0.0005712628,0.0000703223,0.001513907,0.0003661742,0.002424594,0.003046249,0.1898555,0.03508747,0.7634346,0.00049846,0.001621368],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.243525,0.03149235,0.7218771,0.0002839784,0.0006990794,0.0008440807,0.00004279728,0.0007908758,0.0004447205],"genre_scores_gemma":[0.899612,0.02550431,0.07317396,0.00003659821,0.0008540172,0.0001001519,0.00002543391,0.0002783072,0.0004152022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7149415,"threshold_uncertainty_score":0.9996614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005164601511075322,"score_gpt":0.2422460135240934,"score_spread":0.2370814120130181,"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."}}