{"id":"W4416965639","doi":"10.1109/tip.2025.3636676","title":"Heterospectral Structure Compensation Sampling for Hyperspectral Fusion Computational Imaging","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Multispectral image; Full spectral imaging; Pattern recognition (psychology); Interpolation (computer graphics); Residual; Sampling (signal processing); Fusion; Computational complexity theory","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":[0.0005764079,0.0007910904,0.0005005529,0.0004620538,0.0004319948,0.0004972424,0.001016966,0.0005189003,0.00146784],"category_scores_gemma":[0.001347706,0.0002471426,0.0006323779,0.0007157439,0.000565675,0.001244552,0.001322107,0.0008496827,0.0003409988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006390023,"about_ca_system_score_gemma":0.0007129224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002178173,"about_ca_topic_score_gemma":0.003438948,"domain_scores_codex":[0.999703,0.00007655578,0.00001344287,0.00007653599,0.0001026369,0.0000278025],"domain_scores_gemma":[0.9996804,0.0001114235,0.00005580894,0.00005921678,0.00007318917,0.00002001726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001095977,0.00009405083,0.001311005,0.0001046132,0.00006094674,0.0001045004,0.0001401149,0.722205,0.02443471,0.0263818,0.002261532,0.2227921],"study_design_scores_gemma":[0.000001358975,0.00001223801,0.00008548706,0.000002016081,0.00000353158,0.000009211414,0.000005949628,0.994337,0.002859388,0.002093042,0.0005869338,0.000003753164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01590893,0.0001385926,0.9821647,0.00007809661,0.00001655889,0.00002596706,0.00004743209,0.0003079037,0.001311886],"genre_scores_gemma":[0.4530426,0.0003178658,0.5428696,0.0001470298,0.00005569652,0.0001950196,0.0004547205,0.0001110796,0.002806415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002178173,"threshold_uncertainty_score":0.004910469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00978297910938482,"score_gpt":0.2789766691427696,"score_spread":0.2691936900333848,"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."}}