{"id":"W3035441328","doi":"10.1109/tip.2020.2999855","title":"MEF-GAN: Multi-Exposure Image Fusion via Generative Adversarial Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":269,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Hubei Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Discriminator; Computer science; Artificial intelligence; Generator (circuit theory); Ground truth; Image (mathematics); Code (set theory); Representation (politics); Distortion (music); Pattern recognition (psychology); Computer vision; Luminance; Fusion rules; Adversarial system; Image fusion; Power (physics); Detector","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009408313,0.001374827,0.0008713155,0.000412803,0.0002125034,0.0006833724,0.0017527,0.001178944,0.003725848],"category_scores_gemma":[0.001518086,0.0004785827,0.0009385814,0.0002878593,0.0005852113,0.001195226,0.00177176,0.002074656,0.001318772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005682471,"about_ca_system_score_gemma":0.0003599812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001553013,"about_ca_topic_score_gemma":0.002474436,"domain_scores_codex":[0.9996318,0.00008398946,0.0000119631,0.000113139,0.0001093144,0.00004996025],"domain_scores_gemma":[0.9996352,0.000155776,0.00003037958,0.00008740035,0.00006775335,0.00002339484],"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":[0.0002305634,0.0001214743,0.0009769109,0.0001353747,0.0001927956,0.000259744,0.00008279079,0.7150257,0.02187077,0.009986166,0.008279166,0.2428386],"study_design_scores_gemma":[0.000006705734,0.00003605333,0.0001191772,0.00001037046,0.00001030174,0.00007948252,0.000005088955,0.990018,0.003814381,0.004638098,0.00125368,0.000008716249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007129318,0.0004160611,0.987448,0.0001601503,0.00006360236,0.00004952488,0.0001356595,0.002418119,0.002179673],"genre_scores_gemma":[0.531948,0.0006098676,0.4538744,0.0009627703,0.0001076409,0.0002346017,0.001296237,0.0007126628,0.01025385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003725848,"threshold_uncertainty_score":0.01246423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01247397889139607,"score_gpt":0.2429420180283638,"score_spread":0.2304680391369678,"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."}}