{"id":"W2592595259","doi":"10.1364/ao.56.002099","title":"Background noise removal in x-ray ptychography","year":2017,"lang":"en","type":"article","venue":"Applied Optics","topic":"Advanced X-ray Imaging Techniques","field":"Physics and Astronomy","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada)","funders":"China Scholarship Council; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Ptychography; Optics; Noise (video); Diffraction; Image resolution; Spatial frequency; Resolution (logic); Image quality; Background noise; Physics; Materials science; Computer science; Acoustics; Image (mathematics); Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006393812,0.0005932616,0.0005675509,0.0005721101,0.0003980011,0.0006277263,0.0007368082,0.0005759606,0.0009724341],"category_scores_gemma":[0.00173114,0.0003679496,0.0002917542,0.0007981212,0.0006203795,0.0009576871,0.0008690705,0.0006491508,0.0004685147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002940222,"about_ca_system_score_gemma":0.0003308948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004710154,"about_ca_topic_score_gemma":0.000571323,"domain_scores_codex":[0.9992914,0.00009640877,0.00003573417,0.0001483032,0.0003775042,0.00005059305],"domain_scores_gemma":[0.999413,0.000206408,0.00007517807,0.0001393523,0.0001445268,0.00002151769],"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":[0.000276558,0.00006248326,0.00143549,0.000699393,0.00003972852,0.0005331277,0.0002659618,0.01851588,0.8413971,0.008670107,0.001245394,0.1268587],"study_design_scores_gemma":[0.00002031454,0.000137301,0.002598862,0.00005319848,0.00002813286,0.0006646124,0.00005809313,0.09809575,0.8876107,0.002102115,0.008589085,0.00004181853],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1379155,0.001661647,0.8555402,0.000162175,0.00007788859,0.00007790241,0.0001379788,0.0009162251,0.003510396],"genre_scores_gemma":[0.4713068,0.002802935,0.5215163,0.0001624403,0.00003808235,0.0001876952,0.0004609461,0.0004478192,0.003077037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009724341,"threshold_uncertainty_score":0.003381371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01796679437560155,"score_gpt":0.2923392201281061,"score_spread":0.2743724257525045,"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."}}