{"id":"W3131800903","doi":"10.3390/app11041649","title":"Demosaicing by Differentiable Deep Restoration","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Demosaicing; Artificial intelligence; Computer science; Computer vision; Image restoration; Color filter array; Minification; Image (mathematics); Subspace topology; Convolutional neural network; Color image; Mathematics; Color gel; Image processing","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":[0.0006421274,0.00008295663,0.0001048764,0.00004712811,0.0004694543,0.0005301766,0.00058739,0.00003389331,0.00002628777],"category_scores_gemma":[0.00004310186,0.00007183164,0.00002514633,0.0007979488,0.0001006908,0.0004162653,0.0001572216,0.00007592758,0.00004423698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001765715,"about_ca_system_score_gemma":0.00008973138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001161697,"about_ca_topic_score_gemma":0.00000643771,"domain_scores_codex":[0.9987652,0.0000782242,0.0001396963,0.0004070186,0.0003574834,0.000252384],"domain_scores_gemma":[0.9994631,0.0001259687,0.00005136451,0.0002570719,0.00004645923,0.00005600506],"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":[0.000002378728,0.00004592653,0.0001653752,0.000007252808,0.000005484344,0.00001138898,0.0005766351,0.0002192797,0.7788803,0.1493622,0.006523951,0.06419979],"study_design_scores_gemma":[0.0003608419,0.00004237909,0.0004042294,0.00001310963,0.000007026732,0.00001892176,0.000187371,0.04056044,0.9016173,0.04794913,0.008535126,0.0003041051],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02532327,0.0003702048,0.9484634,0.0005180957,0.0001979695,0.0000461602,2.725964e-7,0.00008720803,0.02499338],"genre_scores_gemma":[0.775979,0.000009950347,0.2222358,0.00085579,0.00004341378,0.000007442937,0.000002492741,0.000003537469,0.0008626123],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7506558,"threshold_uncertainty_score":0.5112505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0225828240584039,"score_gpt":0.2709209141159076,"score_spread":0.2483380900575037,"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."}}