{"id":"W4285891871","doi":"10.36227/techrxiv.20329914","title":"BUSIFusion: Blind Unsupervised Single Image Fusion of Hyperspectral and RGB Images","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"IONICS Mass Spectrometry (Canada)","funders":"Natural Science Foundation of Zhejiang Province","keywords":"Artificial intelligence; Regularization (linguistics); Computer science; RGB color model; Hyperspectral imaging; Pattern recognition (psychology); Fusion; Image (mathematics); Artificial neural network; Computer vision; Degradation (telecommunications); Representation (politics)","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":[0.0005244006,0.0007310205,0.0007394284,0.0004766953,0.000356605,0.0006382436,0.001296119,0.0008036015,0.002613986],"category_scores_gemma":[0.0009494369,0.0004203522,0.0004530317,0.0004921291,0.000884501,0.001454908,0.001785991,0.001148064,0.001133693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004892232,"about_ca_system_score_gemma":0.0006870826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002439812,"about_ca_topic_score_gemma":0.003781473,"domain_scores_codex":[0.9996852,0.00005188286,0.000009816062,0.0001020403,0.0001165566,0.00003455636],"domain_scores_gemma":[0.9997615,0.00004035808,0.00004265342,0.00008180797,0.00005029499,0.00002334239],"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":[0.0007727663,0.0001549343,0.001205472,0.0002032521,0.0001699927,0.0001748776,0.0001926822,0.1059021,0.212677,0.01803158,0.01035303,0.6501623],"study_design_scores_gemma":[0.00002648451,0.0001014095,0.0008685581,0.00001432397,0.00001853652,0.0001398629,0.00001879379,0.903091,0.08102298,0.009544636,0.005119842,0.00003361723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01295745,0.0001671953,0.9835285,0.0001240159,0.00004009605,0.00004838038,0.0001667177,0.001604539,0.001363042],"genre_scores_gemma":[0.2743293,0.0003183035,0.7119612,0.0002660833,0.00007761508,0.0001716789,0.001070581,0.0004791562,0.01132616],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002613986,"threshold_uncertainty_score":0.008744657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01533767626054556,"score_gpt":0.2483939937039941,"score_spread":0.2330563174434485,"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."}}