{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009206434,0.0004248784,0.0003449797,0.0001358621,0.0004092221,0.0001620772,0.0002526116,0.0001926712,0.0002329234],"category_scores_gemma":[0.00001428849,0.0004441262,0.0001460079,0.0005501086,0.0001195081,0.001145122,0.00000394758,0.0008155978,0.00006774588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001197447,"about_ca_system_score_gemma":0.00003902272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005728297,"about_ca_topic_score_gemma":0.000007516003,"domain_scores_codex":[0.9982999,0.00005217186,0.0004403435,0.0004871904,0.0002752482,0.0004451626],"domain_scores_gemma":[0.9992264,0.00004254948,0.00008498726,0.0002458247,0.0001761096,0.0002241403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007665569,0.00008183822,8.688401e-7,0.0001096627,0.00003051178,0.0000372943,0.0008775787,0.1572012,0.6410043,3.662361e-7,0.0004329285,0.2001468],"study_design_scores_gemma":[0.0005745672,0.00008065336,0.000003517379,0.0000589683,0.00003655821,0.000009102556,0.00009008704,0.562203,0.4363379,0.00001525487,0.0002680974,0.0003222325],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006793462,0.0003709168,0.995023,0.0002415482,0.0004564947,0.0004112017,0.00002825366,0.00229259,0.000496661],"genre_scores_gemma":[0.7127944,0.0001425882,0.2861301,0.0003690046,0.0002778054,0.00008393497,0.000009687074,0.0001326515,0.00005977827],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7121151,"threshold_uncertainty_score":0.999801,"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."}}