{"id":"W4399728165","doi":"10.1109/tnnls.2024.3409563","title":"VOGTNet: Variational Optimization-Guided Two-Stage Network for Multispectral and Panchromatic Image Fusion","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Panchromatic film; Multispectral image; Artificial intelligence; Computer science; Robustness (evolution); Image resolution; Computer vision; Noise (video); Image fusion; Pattern recognition (psychology); Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006880403,0.00157825,0.001101022,0.0004604535,0.0003922007,0.0005945703,0.002196655,0.001739755,0.001923187],"category_scores_gemma":[0.001626687,0.0006473603,0.0009332689,0.0004682375,0.0006978143,0.00110432,0.001279738,0.001688374,0.0004091696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001196668,"about_ca_system_score_gemma":0.001260025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01645497,"about_ca_topic_score_gemma":0.02025609,"domain_scores_codex":[0.999742,0.00004275147,0.00001226241,0.00009141971,0.00005971803,0.00005182982],"domain_scores_gemma":[0.9996743,0.0001372112,0.0000394746,0.00003406446,0.00008206213,0.00003283664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000128591,0.0000974247,0.001040107,0.0000795321,0.0001017349,0.0001017837,0.00006562016,0.8752406,0.005492108,0.003824744,0.003334074,0.1104937],"study_design_scores_gemma":[0.000003578697,0.0000171988,0.00004131127,0.000002610676,0.000004653643,0.000007895093,0.000002429239,0.9985253,0.0004016572,0.0008234298,0.0001670442,0.000002958885],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03975359,0.0009325364,0.9530723,0.0003351274,0.0001380851,0.00008210817,0.0002129899,0.002414866,0.003058287],"genre_scores_gemma":[0.7280101,0.0004736341,0.259261,0.0006971455,0.00008985437,0.0002769167,0.001705443,0.0003358219,0.00915009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01645497,"threshold_uncertainty_score":0.03271836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009460183352951816,"score_gpt":0.2469800435162106,"score_spread":0.2375198601632588,"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."}}