{"id":"W4391102977","doi":"10.3390/jimaging10010028","title":"Endoscopic Image Enhancement: Wavelet Transform and Guided Filter Decomposition-Based Fusion Approach","year":2024,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Kermanshah University of Medical Sciences","keywords":"Artificial intelligence; Image fusion; Computer science; Computer vision; Filter (signal processing); Wavelet transform; Endoscope; Fusion; Wavelet; Image quality; Process (computing); Set (abstract data type); Image (mathematics); Pattern recognition (psychology); Radiology; Medicine","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":[0.0007435346,0.00054104,0.0005951546,0.0007324471,0.000173897,0.00049483,0.0004906039,0.0007469574,0.0006542765],"category_scores_gemma":[0.0008114417,0.0002404236,0.0009120178,0.0006833315,0.0003117289,0.0008526982,0.0005784368,0.0006827153,0.0003180653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002676461,"about_ca_system_score_gemma":0.0003565083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009371391,"about_ca_topic_score_gemma":0.0007107825,"domain_scores_codex":[0.9997033,0.00005147763,0.00001829084,0.00005640372,0.0001383412,0.00003209515],"domain_scores_gemma":[0.999805,0.00005112687,0.00003054238,0.00002466322,0.00007614816,0.00001254999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003895412,0.0001803577,0.00123905,0.0002281869,0.0001585691,0.0002024651,0.0001746694,0.1327974,0.1983973,0.009806529,0.001507539,0.6549184],"study_design_scores_gemma":[0.00001309121,0.0001499555,0.0009265803,0.00001618363,0.00006383314,0.0002473204,0.00002454583,0.9611945,0.03329768,0.002122837,0.001923643,0.00001975223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01264922,0.0003422213,0.98619,0.0000547445,0.00001885252,0.00002307074,0.00001365124,0.0001447699,0.0005634756],"genre_scores_gemma":[0.2888072,0.001229245,0.7072652,0.00008324961,0.00005411847,0.00007480245,0.0001321874,0.00006249782,0.002291461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009371391,"threshold_uncertainty_score":0.003932238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01016969402876454,"score_gpt":0.2826852640875969,"score_spread":0.2725155700588323,"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."}}