{"id":"W2804199492","doi":"10.1007/s12652-018-0866-4","title":"Pansharpening using a guided image filter based on dual-scale detail extraction","year":2018,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China; Alberta Innovates - Health Solutions","keywords":"Panchromatic film; Multispectral image; Computer science; Artificial intelligence; Computer vision; Filter (signal processing); Image resolution; Image (mathematics); Composite image filter; Visibility; Image processing; Pattern recognition (psychology); Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004610954,0.0001852266,0.0002689674,0.0002906027,0.0002225577,0.0001302741,0.0001488129,0.00005885511,0.0001619988],"category_scores_gemma":[0.00007711542,0.0001729232,0.0000953288,0.0001627536,0.00008360517,0.000358607,0.00004731957,0.000350151,0.00001122881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001051029,"about_ca_system_score_gemma":0.00001868833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005045736,"about_ca_topic_score_gemma":0.000001887382,"domain_scores_codex":[0.9986165,0.00003788405,0.0006696222,0.0001581177,0.0002677166,0.0002501141],"domain_scores_gemma":[0.9990104,0.0001164822,0.000294532,0.0001592313,0.0003328458,0.00008649671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000178675,0.0001416422,0.0004774321,0.0001573353,0.00006141162,0.000154683,0.002336878,0.05889989,0.8230044,0.0001101917,0.001266921,0.1132105],"study_design_scores_gemma":[0.0001876547,0.000278377,0.0001670315,0.0005978577,0.00002441984,0.0001862098,0.0002137258,0.6017362,0.3956274,0.0003966076,0.000393454,0.0001911086],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3281193,0.00008250134,0.670936,0.00001228676,0.0002956118,0.00007886213,7.141028e-7,0.00009205987,0.000382596],"genre_scores_gemma":[0.7907985,0.00003189255,0.2086567,0.000110369,0.0003581576,5.515757e-7,6.197618e-7,0.00002914716,0.00001395144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5428363,"threshold_uncertainty_score":0.7051605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04184047020972385,"score_gpt":0.3180938703950822,"score_spread":0.2762534001853584,"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."}}