{"id":"W4404654852","doi":"10.1016/j.engappai.2024.109659","title":"Dual graph-regularized low-rank representation for hyperspectral image denoising","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Key Technology Research and Development Program of Shandong; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Computer science; Image denoising; Graph; Rank (graph theory); Artificial intelligence; Dual (grammatical number); Noise reduction; Representation (politics); Pattern recognition (psychology); Computer vision; Theoretical computer science; Mathematics; Combinatorics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004987214,0.000138939,0.0001687058,0.0003062346,0.0001029248,0.0002817957,0.0004480765,0.00005875657,0.000006614228],"category_scores_gemma":[0.0001383199,0.0001493427,0.0001489428,0.00106365,0.00006071581,0.0004175988,0.00005850503,0.0001294028,0.00002730916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003576948,"about_ca_system_score_gemma":0.00005182164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000213705,"about_ca_topic_score_gemma":8.403413e-7,"domain_scores_codex":[0.9987141,0.00002486733,0.0004110637,0.0004177915,0.0001941468,0.0002379566],"domain_scores_gemma":[0.9987962,0.000446874,0.00005228145,0.0004816424,0.000163903,0.00005912583],"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":[0.000009047955,0.00003604088,3.76981e-7,0.00008621187,0.00002346151,0.000003188002,0.0004557189,0.01005445,0.5551152,0.3236733,0.0000442468,0.1104988],"study_design_scores_gemma":[0.00002013291,0.00002328864,0.000006895591,0.00004030225,0.00001376528,0.000008681201,0.00003639198,0.4210164,0.5353165,0.04314188,0.0002531551,0.0001225638],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003761969,0.0003851827,0.9943357,0.0003699984,0.0002875679,0.0004360766,0.000007690603,0.0003472324,0.00006862831],"genre_scores_gemma":[0.2864507,0.00001537463,0.7130707,0.00001202416,0.0001891164,0.0001791147,0.000007758976,0.00002030313,0.00005488039],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.410962,"threshold_uncertainty_score":0.6090019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02651948368315956,"score_gpt":0.3160362983010485,"score_spread":0.289516814617889,"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."}}