{"id":"W1968396256","doi":"10.1117/12.391917","title":"&lt;title&gt;Improving spatial resolution of infrared images by means of sensor fusion&lt;/title&gt;","year":2000,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Wavelet; Artificial intelligence; Image fusion; Computer vision; Computer science; Infrared; Transformation (genetics); Wavelet transform; Pattern recognition (psychology); Image resolution; Merge (version control); Image (mathematics); Optics; Physics","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":[0.0002043704,0.0002005367,0.0002921562,0.00008511356,0.00002949971,0.00002060993,0.0004004177,0.000150047,0.0002984618],"category_scores_gemma":[0.0002169937,0.0001853048,0.0002728148,0.0001948347,0.0001443074,0.0002422989,0.00006819062,0.0001866567,0.00000701512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000087578,"about_ca_system_score_gemma":0.00001487615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005047906,"about_ca_topic_score_gemma":6.815702e-8,"domain_scores_codex":[0.9986674,2.277949e-8,0.0005060475,0.0001889926,0.0004177778,0.0002197804],"domain_scores_gemma":[0.9990692,0.0000530045,0.0001736673,0.00006112782,0.0005860961,0.00005693593],"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":[0.00002721627,0.00003606223,0.00001253629,0.0003915057,0.00009179551,6.130054e-8,0.00006129515,0.0002005753,0.9480439,0.01767092,0.02666865,0.006795446],"study_design_scores_gemma":[0.0005528191,0.0001796665,0.0001444599,0.0003538701,0.00009031478,0.000007853244,0.0001232122,0.07687569,0.8699983,0.001004085,0.05030635,0.0003633408],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9312162,0.00037012,0.001857824,0.0002250392,0.0002188091,0.0004335445,0.0001804273,0.0002748456,0.06522323],"genre_scores_gemma":[0.4742925,0.001302809,0.5176412,0.00005833513,0.0007460738,0.0001277211,0.00004561674,0.0002386539,0.005547082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5157834,"threshold_uncertainty_score":0.755651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005738803899624391,"score_gpt":0.2090764176093737,"score_spread":0.2033376137097493,"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."}}