{"id":"W2131813700","doi":"10.1109/ccece.1996.548130","title":"Image denoising for reduced-search fractal block coding","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Research Manitoba","funders":"","keywords":"Non-local means; Artificial intelligence; Smoothing; Noise reduction; Wavelet; Lossy compression; Wavelet transform; Mathematics; Computer vision; Image compression; Fractal transform; Peak signal-to-noise ratio; Pattern recognition (psychology); Entropy (arrow of time); Computer science; Image processing; Image denoising; 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":[0.0007193408,0.0001300865,0.0001596647,0.0001293455,0.0003004855,0.0005053331,0.0006682981,0.00005690792,0.0001004963],"category_scores_gemma":[0.0001574394,0.0001176881,0.0001055979,0.0003063013,0.00004295705,0.0007126355,0.000173018,0.0001459805,0.0001467659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003770949,"about_ca_system_score_gemma":0.000021015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001669117,"about_ca_topic_score_gemma":6.241106e-7,"domain_scores_codex":[0.9985851,0.00009592024,0.0002108007,0.000389746,0.0002698691,0.0004485874],"domain_scores_gemma":[0.9989161,0.0003770031,0.00003829288,0.0004152218,0.0001436645,0.0001097527],"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":[0.00001499601,0.0001254163,0.00002320421,0.00004424977,0.00002692932,0.00007692557,0.001554312,0.0000313602,0.7463019,0.01636036,0.02051199,0.2149283],"study_design_scores_gemma":[0.001118212,0.0001782823,0.0001587803,0.00004061781,0.00001201336,0.000148122,0.00006158389,0.5903009,0.3984319,0.003221057,0.005869742,0.000458791],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01247142,0.0001189581,0.967219,0.00125035,0.0002755364,0.0001760791,9.310139e-7,0.0002066974,0.01828103],"genre_scores_gemma":[0.3302323,0.000008636591,0.6651321,0.0004661907,0.000167628,0.000007369706,5.225887e-7,0.00001360303,0.003971625],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5902695,"threshold_uncertainty_score":0.4872939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06025614976125717,"score_gpt":0.3237847055207166,"score_spread":0.2635285557594594,"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."}}