{"id":"W2039117831","doi":"10.1109/ccece.2014.6901077","title":"Contourlet domain image denoising using normal inverse gaussian distribution","year":2014,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Contourlet; Generalized normal distribution; Gaussian noise; Mathematics; Noise reduction; Additive white Gaussian noise; Maximum a posteriori estimation; Noise (video); Gaussian; Gradient noise; Normal-inverse Gaussian distribution; Artificial intelligence; Pattern recognition (psychology); Value noise; Algorithm; Computer science; Noise measurement; Normal distribution; White noise; Wavelet; Image (mathematics); Statistics; Gaussian random field; Gaussian process; Wavelet transform; Noise floor; Physics","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":[0.00124614,0.0001582554,0.0001843717,0.00007033783,0.0002920672,0.0004427351,0.0005111934,0.00007014366,0.00003962389],"category_scores_gemma":[0.0001111418,0.0001406068,0.00008322442,0.0003133109,0.00007818385,0.001103953,0.0002082029,0.0001416684,0.00007829414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008345781,"about_ca_system_score_gemma":0.00005202144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001702508,"about_ca_topic_score_gemma":0.00001303802,"domain_scores_codex":[0.9983793,0.0003635219,0.0002453575,0.000340136,0.0002660323,0.0004056196],"domain_scores_gemma":[0.9991024,0.0001113107,0.0000892668,0.0004653635,0.00009001845,0.0001417063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005995625,0.0001464096,0.0006613777,0.00005807026,0.00004354746,0.0001834053,0.001468194,0.000383943,0.6403573,0.2580698,0.008495939,0.09007215],"study_design_scores_gemma":[0.003208368,0.0002143363,0.003087654,0.000104317,0.00003985393,0.000327322,0.0001619167,0.7660419,0.1575752,0.0423541,0.02572602,0.001159038],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04628703,0.00001219093,0.9442264,0.0003807553,0.0003048853,0.00007486134,0.000002182137,0.0001689904,0.008542708],"genre_scores_gemma":[0.3324386,9.577714e-7,0.6663446,0.000726383,0.0001826437,0.000001422745,0.000008805818,0.00001004505,0.0002864819],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.765658,"threshold_uncertainty_score":0.573378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0175339630991041,"score_gpt":0.2708767386366358,"score_spread":0.2533427755375317,"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."}}