{"id":"W4412930994","doi":"10.64539/sjer.v1i3.2025.27","title":"Effectiveness of Fourier, Wiener, Bilateral, and CLAHE Denoising Methods for CT Scan Image Noise Reduction","year":2025,"lang":"en","type":"article","venue":"Scientific Journal of Engineering Research","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Noise reduction; Fourier transform; Noise (video); Reduction (mathematics); Adaptive histogram equalization; Image denoising; Computer science; Computer vision; Medicine; Mathematics; Image (mathematics); Artificial intelligence; Image processing; Histogram equalization; Mathematical analysis","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":[0.002737425,0.0010191,0.0008484315,0.001987383,0.0004437936,0.001276134,0.0005641123,0.001170255,0.001013315],"category_scores_gemma":[0.006528243,0.0002834342,0.001102017,0.0008169556,0.0006195595,0.001416923,0.0007260096,0.000701139,0.000429062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003597923,"about_ca_system_score_gemma":0.0008153917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002289053,"about_ca_topic_score_gemma":0.004436306,"domain_scores_codex":[0.998323,0.000242743,0.0001365695,0.0002778567,0.0009102233,0.0001094959],"domain_scores_gemma":[0.9980633,0.0008004045,0.0001974697,0.0001657895,0.0007136193,0.00005941116],"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":[0.001003018,0.0002613303,0.009078654,0.0008460634,0.0003455965,0.0003919524,0.0003781613,0.04954947,0.1594275,0.002649751,0.003027484,0.773041],"study_design_scores_gemma":[0.00008501964,0.001562479,0.02573642,0.0002828973,0.0006168874,0.002731093,0.0007550025,0.5939807,0.3549093,0.004670285,0.01443563,0.000234238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2895094,0.008091026,0.6929369,0.0007663629,0.0003606077,0.0001914992,0.0003764018,0.0014321,0.00633561],"genre_scores_gemma":[0.5610114,0.004132241,0.4286602,0.0002960352,0.0001622314,0.0001439172,0.0006558151,0.0002395565,0.004698659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002737425,"threshold_uncertainty_score":0.01447701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02305832550255056,"score_gpt":0.3970704881578866,"score_spread":0.374012162655336,"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."}}