{"id":"W4387885618","doi":"10.1109/tgrs.2023.3324993","title":"BrGAN: Blur Resist Generative Adversarial Network With Multiple Joint Dilated Residual Convolutions for Chlorophyll Color Image Restoration","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Huaqiao University; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Residual; Joint (building); Computer vision; Image restoration; Convolution (computer science); Convolutional neural network; Pattern recognition (psychology); Image (mathematics); Image processing; Artificial neural network; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001996223,0.000196651,0.0001943197,0.0001994164,0.0008990766,0.00008075574,0.00006188588,0.00009763307,0.000002631073],"category_scores_gemma":[0.00002905078,0.0001793542,0.00004620034,0.0007798819,0.0002643877,0.0003326253,0.000002628738,0.0002140081,0.000007796443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001006933,"about_ca_system_score_gemma":0.00005313927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001098719,"about_ca_topic_score_gemma":0.0005109077,"domain_scores_codex":[0.9987974,0.00003556067,0.0002409824,0.000350871,0.0001955198,0.0003796637],"domain_scores_gemma":[0.9993599,0.0001408742,0.00005338195,0.000207871,0.0001472905,0.0000906951],"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":[0.0003180767,0.00002168617,5.17649e-7,0.00005175731,0.0000393205,0.00003530632,0.0008242946,0.269453,0.6492406,0.00001373306,0.001580441,0.07842123],"study_design_scores_gemma":[0.0005372429,0.0001957658,0.0001235433,0.0001011287,0.00002916986,0.00001944968,0.0001911559,0.8162205,0.1816525,0.0001737495,0.0005164199,0.0002393356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06660514,0.00001495331,0.9311219,0.0002266926,0.0005634875,0.0005417319,0.00005084423,0.0008167493,0.00005849901],"genre_scores_gemma":[0.4899498,0.0001572409,0.509011,0.0000685226,0.0002025242,0.000004487422,0.00001981346,0.00005391473,0.0005326693],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5467675,"threshold_uncertainty_score":0.7313852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01469798218660256,"score_gpt":0.2298858371080125,"score_spread":0.2151878549214099,"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."}}