{"id":"W4385804995","doi":"10.1109/cvprw59228.2023.00313","title":"Image Inpainting with Hypergraphs for Resolution Improvement in Scanning Acoustic Microscopy","year":2023,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Inpainting; Artificial intelligence; Computer science; Interpretability; Computer vision; Noise (video); Image (mathematics); Pixel; Enhanced Data Rates for GSM Evolution; Resolution (logic); Pattern recognition (psychology); Image resolution","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":[0.001234942,0.0001024542,0.0001239554,0.0002719525,0.0001374151,0.0001836939,0.0003017822,0.00003226066,0.000002031483],"category_scores_gemma":[0.00009476264,0.00008393641,0.00003731745,0.0009271087,0.00002971093,0.0003551788,0.0001209985,0.00008429172,0.00001051655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004131414,"about_ca_system_score_gemma":0.00004501631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001017173,"about_ca_topic_score_gemma":0.00003601633,"domain_scores_codex":[0.9989004,0.00004808635,0.0001889333,0.0003248428,0.0001510256,0.0003867541],"domain_scores_gemma":[0.9994218,0.0001726847,0.00004955275,0.0002411827,0.00007597722,0.00003880139],"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":[0.00003403599,0.00002186863,0.0003470903,0.00005058973,0.00000731148,0.00002642412,0.0005159281,0.001027453,0.9686393,0.0008915635,0.001120082,0.02731841],"study_design_scores_gemma":[0.001585127,0.0003821201,0.002850835,0.0001262519,0.000008192045,0.000007751897,0.0002320164,0.8652433,0.1258395,0.003119635,0.0003067472,0.0002985601],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08328147,0.00001806487,0.9151835,0.0003611774,0.0001243799,0.0002379438,6.697628e-7,0.0002137832,0.0005789913],"genre_scores_gemma":[0.2632872,0.000002965814,0.7356973,0.0003168462,0.00003411953,0.00005142285,0.000002629243,0.00001248266,0.0005950712],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8642158,"threshold_uncertainty_score":0.3422828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0177827906441445,"score_gpt":0.2962088822101984,"score_spread":0.2784260915660539,"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."}}