{"id":"W4398132044","doi":"10.1088/1361-6501/ad4dca","title":"Underwater image enhancement via color correction and multi-feature image fusion","year":2024,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"PricewaterhouseCoopers (Canada)","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Underwater; Computer vision; Feature (linguistics); Image (mathematics); Image fusion; Color correction; Computer science; Color image; Feature detection (computer vision); Image enhancement; Pattern recognition (psychology); Image processing; Geology","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":[0.000415536,0.000620171,0.0005986326,0.001011794,0.0002394183,0.0004442751,0.0005478327,0.0004488454,0.001153138],"category_scores_gemma":[0.0006807869,0.0002678,0.0007352974,0.0007604496,0.0003247859,0.001091449,0.0007940741,0.0005532728,0.0003450392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000286157,"about_ca_system_score_gemma":0.0003112253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001076908,"about_ca_topic_score_gemma":0.0009016293,"domain_scores_codex":[0.9995942,0.00004336962,0.0000214723,0.00009411438,0.0002029461,0.000043971],"domain_scores_gemma":[0.9996603,0.00005566711,0.00005359241,0.00005844009,0.0001552605,0.0000167584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000258021,0.0000986233,0.001530036,0.0002045484,0.00009043686,0.0001740527,0.0001106443,0.0304054,0.5291244,0.002760471,0.001341127,0.4339021],"study_design_scores_gemma":[0.00002576608,0.0001739253,0.003202001,0.00001795955,0.00008963586,0.0004731018,0.00004797157,0.5915288,0.3983816,0.001465675,0.004531024,0.00006249326],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05452551,0.0003836783,0.9423854,0.00008410646,0.0000571712,0.00004216763,0.00003491814,0.000994116,0.001492918],"genre_scores_gemma":[0.5222393,0.000447778,0.4744846,0.0001049664,0.00005661904,0.00005181365,0.0001233423,0.0001401573,0.002351482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001153138,"threshold_uncertainty_score":0.003857613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01507270899752574,"score_gpt":0.2558000399511282,"score_spread":0.2407273309536025,"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."}}