{"id":"W2111313369","doi":"10.1109/tip.2010.2045691","title":"Noise Reduction of cDNA Microarray Images Using Complex Wavelets","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Noise reduction; Pattern recognition (psychology); Artificial intelligence; Wavelet; Complex wavelet transform; Noise (video); Mathematics; Wavelet transform; Signal-to-noise ratio (imaging); Computer science; Discrete wavelet transform; Statistics; Image (mathematics)","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":[0.0005150091,0.0002408455,0.0002832013,0.0003602637,0.0004971496,0.0003264286,0.0005555936,0.0001104512,0.00004984219],"category_scores_gemma":[0.00001671472,0.0002393388,0.0001443802,0.0006947789,0.000258749,0.001502863,0.000005358369,0.000537712,0.00001523835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003990339,"about_ca_system_score_gemma":0.0001769346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005233327,"about_ca_topic_score_gemma":0.000002584334,"domain_scores_codex":[0.9982603,0.0001167994,0.0004264552,0.0005033595,0.0003370547,0.0003560042],"domain_scores_gemma":[0.9987218,0.00006493584,0.0002188384,0.000524735,0.0003640208,0.0001056604],"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":[0.00002178866,0.0001210834,9.549722e-7,0.00006132609,0.000009116512,0.00000698655,0.0004180427,0.0002782542,0.8040162,0.00001275708,0.0000311094,0.1950224],"study_design_scores_gemma":[0.0004903775,0.00004603772,0.00007489887,0.00007808181,0.00003245772,0.0002391119,0.00003874941,0.05632273,0.9418582,0.0004852903,0.00008094458,0.000253158],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04598549,0.0000430599,0.9520069,0.0002512512,0.0008360033,0.0001451273,0.000007194783,0.0001802734,0.0005446381],"genre_scores_gemma":[0.5299702,0.000003502047,0.4697834,0.00004778385,0.00005572251,0.00000435604,5.496763e-7,0.00001778282,0.0001166764],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4839847,"threshold_uncertainty_score":0.9759952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03204848885254596,"score_gpt":0.3049723650029289,"score_spread":0.2729238761503829,"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."}}