{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:5FZK3LYSF3T2UVYJVFE6XPTWPA","short_pith_number":"pith:5FZK3LYS","canonical_record":{"source":{"id":"1811.06950","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.optics","submitted_at":"2018-11-16T18:04:40Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"6780fb98bcefbfb91834865f7866710bc307b842000c1f2989ae1b65cd7ee4bb","abstract_canon_sha256":"bb4574186f792b5b965254994a0d966b398718d06953d505950d8d565b5756d3"},"schema_version":"1.0"},"canonical_sha256":"e972adaf122ee7aa5709a949ebbe767826207c03acfd4184bb7aa53dfe5dc399","source":{"kind":"arxiv","id":"1811.06950","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.06950","created_at":"2026-05-17T23:50:22Z"},{"alias_kind":"arxiv_version","alias_value":"1811.06950v1","created_at":"2026-05-17T23:50:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.06950","created_at":"2026-05-17T23:50:22Z"},{"alias_kind":"pith_short_12","alias_value":"5FZK3LYSF3T2","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5FZK3LYSF3T2UVYJ","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5FZK3LYS","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:5FZK3LYSF3T2UVYJVFE6XPTWPA","target":"record","payload":{"canonical_record":{"source":{"id":"1811.06950","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.optics","submitted_at":"2018-11-16T18:04:40Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"6780fb98bcefbfb91834865f7866710bc307b842000c1f2989ae1b65cd7ee4bb","abstract_canon_sha256":"bb4574186f792b5b965254994a0d966b398718d06953d505950d8d565b5756d3"},"schema_version":"1.0"},"canonical_sha256":"e972adaf122ee7aa5709a949ebbe767826207c03acfd4184bb7aa53dfe5dc399","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:22.944215Z","signature_b64":"W1tIxGrfKs7pLMTzc4N0it3tyxozT5p5ZirnWenAKjNz0cewCVuEB2bwcHnKJpQUZ0+2DHfpahqP/QkJorjjCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e972adaf122ee7aa5709a949ebbe767826207c03acfd4184bb7aa53dfe5dc399","last_reissued_at":"2026-05-17T23:50:22.943590Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:22.943590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.06950","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:50:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1WN6lp1daql2+caB/bl4WLeYsSp3Skxv+B0V+3Z/vGErXUGBFBM3FIZcJ6yyvAPD0lMwtsd788xCAWiTNf+JAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T15:43:38.027436Z"},"content_sha256":"3ce2b3696a948978adf363a7008a0c79c7a20eb9c2f5d6adae8bfae37bc331d2","schema_version":"1.0","event_id":"sha256:3ce2b3696a948978adf363a7008a0c79c7a20eb9c2f5d6adae8bfae37bc331d2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:5FZK3LYSF3T2UVYJVFE6XPTWPA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep learning approach to coherent noise reduction in optical diffraction tomography","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"physics.optics","authors_text":"Donghun Ryu, Gunho Choi, Hyun-seok Min, Weisun Park, YongKeun Park, YoungJu Jo, Youngseo Kim","submitted_at":"2018-11-16T18:04:40Z","abstract_excerpt":"We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images. The learned network quantitatively demonstrated its performance and generalization capability through denoising experiments of various samples. We concl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.06950","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:50:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EdTHdgnQFWQbl1vyb+WGadU0TuIP0ioT6F41f3sOX4KPTIaUJ/qlR5mJ73rbzU+X/8ttz6o2eoaM4Byz0xb9Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T15:43:38.028143Z"},"content_sha256":"13feabf70b091163ddf40b9343142542b97e46b9aeb2ffc08b2edefccb7b4921","schema_version":"1.0","event_id":"sha256:13feabf70b091163ddf40b9343142542b97e46b9aeb2ffc08b2edefccb7b4921"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5FZK3LYSF3T2UVYJVFE6XPTWPA/bundle.json","state_url":"https://pith.science/pith/5FZK3LYSF3T2UVYJVFE6XPTWPA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5FZK3LYSF3T2UVYJVFE6XPTWPA/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-06T15:43:38Z","links":{"resolver":"https://pith.science/pith/5FZK3LYSF3T2UVYJVFE6XPTWPA","bundle":"https://pith.science/pith/5FZK3LYSF3T2UVYJVFE6XPTWPA/bundle.json","state":"https://pith.science/pith/5FZK3LYSF3T2UVYJVFE6XPTWPA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5FZK3LYSF3T2UVYJVFE6XPTWPA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:5FZK3LYSF3T2UVYJVFE6XPTWPA","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"bb4574186f792b5b965254994a0d966b398718d06953d505950d8d565b5756d3","cross_cats_sorted":["eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.optics","submitted_at":"2018-11-16T18:04:40Z","title_canon_sha256":"6780fb98bcefbfb91834865f7866710bc307b842000c1f2989ae1b65cd7ee4bb"},"schema_version":"1.0","source":{"id":"1811.06950","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.06950","created_at":"2026-05-17T23:50:22Z"},{"alias_kind":"arxiv_version","alias_value":"1811.06950v1","created_at":"2026-05-17T23:50:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.06950","created_at":"2026-05-17T23:50:22Z"},{"alias_kind":"pith_short_12","alias_value":"5FZK3LYSF3T2","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5FZK3LYSF3T2UVYJ","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5FZK3LYS","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:13feabf70b091163ddf40b9343142542b97e46b9aeb2ffc08b2edefccb7b4921","target":"graph","created_at":"2026-05-17T23:50:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images. The learned network quantitatively demonstrated its performance and generalization capability through denoising experiments of various samples. We concl","authors_text":"Donghun Ryu, Gunho Choi, Hyun-seok Min, Weisun Park, YongKeun Park, YoungJu Jo, Youngseo Kim","cross_cats":["eess.IV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.optics","submitted_at":"2018-11-16T18:04:40Z","title":"Deep learning approach to coherent noise reduction in optical diffraction tomography"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.06950","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3ce2b3696a948978adf363a7008a0c79c7a20eb9c2f5d6adae8bfae37bc331d2","target":"record","created_at":"2026-05-17T23:50:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"bb4574186f792b5b965254994a0d966b398718d06953d505950d8d565b5756d3","cross_cats_sorted":["eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.optics","submitted_at":"2018-11-16T18:04:40Z","title_canon_sha256":"6780fb98bcefbfb91834865f7866710bc307b842000c1f2989ae1b65cd7ee4bb"},"schema_version":"1.0","source":{"id":"1811.06950","kind":"arxiv","version":1}},"canonical_sha256":"e972adaf122ee7aa5709a949ebbe767826207c03acfd4184bb7aa53dfe5dc399","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e972adaf122ee7aa5709a949ebbe767826207c03acfd4184bb7aa53dfe5dc399","first_computed_at":"2026-05-17T23:50:22.943590Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:50:22.943590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"W1tIxGrfKs7pLMTzc4N0it3tyxozT5p5ZirnWenAKjNz0cewCVuEB2bwcHnKJpQUZ0+2DHfpahqP/QkJorjjCg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:50:22.944215Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.06950","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3ce2b3696a948978adf363a7008a0c79c7a20eb9c2f5d6adae8bfae37bc331d2","sha256:13feabf70b091163ddf40b9343142542b97e46b9aeb2ffc08b2edefccb7b4921"],"state_sha256":"7ebe2cd13805a695ddce50a20348d7cdcdcdb32e0fe0d992c2f367d70cd8841f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FeNI3cmo5wMH5oCBuuSKSF/JTxzUml5LZL1ThH/PeIPnwhItfoXzd9yWhu372yYLd0aLAvxv6MyKr9qv/eZEBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T15:43:38.031851Z","bundle_sha256":"38451f78d087362ff0fa3249943183650d8d436cdfcbe7e044f055fc15b571b7"}}