{"id":"W4412749426","doi":"10.1016/j.jvcir.2025.104535","title":"Dual-Branch Wavelet Diffusion models with Dual-Prior Refinement for Underwater Image Enhancement","year":2025,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada","keywords":"Dual (grammatical number); Underwater; Wavelet; Image (mathematics); Image enhancement; Computer science; Diffusion; Artificial intelligence; Computer vision; Mathematics; Algorithm; Geology; Physics; Art; Oceanography","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":[],"consensus_categories":[],"category_scores_codex":[0.001173798,0.0001421265,0.0002568848,0.0002476972,0.0003025047,0.0003943281,0.0003240854,0.00004508009,0.00001047714],"category_scores_gemma":[0.00009139786,0.0001089034,0.00008118829,0.0002838726,0.00008463334,0.001433108,0.0002454777,0.0001777216,0.00000168475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006700354,"about_ca_system_score_gemma":0.00008908947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002201708,"about_ca_topic_score_gemma":0.000003527602,"domain_scores_codex":[0.9983312,0.000376941,0.0005863168,0.0002098464,0.0003298264,0.0001659056],"domain_scores_gemma":[0.9979556,0.0003407459,0.0004345744,0.0004907258,0.0007162985,0.00006208938],"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":[0.0009530657,0.0008606295,0.00005365377,0.0001840435,0.0002027943,0.0000152455,0.003495004,0.0001786976,0.6808488,0.01243456,0.004180036,0.2965935],"study_design_scores_gemma":[0.01326786,0.002022933,0.002827811,0.0008553775,0.0002197049,0.000182218,0.001249461,0.3292057,0.5799004,0.06511072,0.004537068,0.0006207631],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03069106,0.0004051159,0.9624458,0.004589485,0.00009216104,0.0003069704,9.968178e-7,0.00001803779,0.001450393],"genre_scores_gemma":[0.418376,0.0008728316,0.5792354,0.0004918168,0.00004121668,0.00002363144,0.00001276649,0.00001055734,0.0009357707],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3876849,"threshold_uncertainty_score":0.444095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03363734240899182,"score_gpt":0.3688717875114133,"score_spread":0.3352344451024215,"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."}}