{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:PEGAVCCHZAMZRSHX5BE543H7ZX","short_pith_number":"pith:PEGAVCCH","schema_version":"1.0","canonical_sha256":"790c0a8847c81998c8f7e849de6cffcdf9a36c88d59872464a57cddf9b8d73b9","source":{"kind":"arxiv","id":"2607.22765","version":1},"attestation_state":"computed","paper":{"title":"Frequency-Aware Dual-Stream Learning for Balanced Realism and Fidelity in Electron Microscopy Imaging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Longmi Gao, Manoranjan Paul, Pan Gao, Zhengkai Zhao","submitted_at":"2026-07-24T03:09:05Z","abstract_excerpt":"Electron microscopy enables nanoscale cellular visualization but faces a trade-off between imaging resolution and acquisition speed. Existing learning-based methods rely on single-stream architectures that struggle to balance perceptual realism and quantitative fidelity, either over-smoothing details or generating unrealistic hallucinations. This work introduces a frequency-adaptive dual-stream architecture to resolve this conflict. Using discrete wavelet transform, we decompose images into low-frequency structures and high-frequency details, then employ a conditional diffusion model for reali"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.22765","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-07-24T03:09:05Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"c52956cfd32294467e5d46540b279f7098d856b3c150b8acbc232a4b20bd7cf6","abstract_canon_sha256":"6411f3f87f9537597cf4b4e03891787d8152cfa8220135fcf58a74c7440d8fc8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T00:21:53.301720Z","signature_b64":"urZkqlKpofze6PfhgSKGwJBfVTaj4107Yow9pEuVFsfi61SeTobJXBeMrsHq5UCqZtzTcyjnxExpvqnKLysoBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"790c0a8847c81998c8f7e849de6cffcdf9a36c88d59872464a57cddf9b8d73b9","last_reissued_at":"2026-07-28T00:21:53.300898Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T00:21:53.300898Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Frequency-Aware Dual-Stream Learning for Balanced Realism and Fidelity in Electron Microscopy Imaging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Longmi Gao, Manoranjan Paul, Pan Gao, Zhengkai Zhao","submitted_at":"2026-07-24T03:09:05Z","abstract_excerpt":"Electron microscopy enables nanoscale cellular visualization but faces a trade-off between imaging resolution and acquisition speed. Existing learning-based methods rely on single-stream architectures that struggle to balance perceptual realism and quantitative fidelity, either over-smoothing details or generating unrealistic hallucinations. This work introduces a frequency-adaptive dual-stream architecture to resolve this conflict. Using discrete wavelet transform, we decompose images into low-frequency structures and high-frequency details, then employ a conditional diffusion model for reali"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22765","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.22765/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.22765","created_at":"2026-07-28T00:21:53.301325+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.22765v1","created_at":"2026-07-28T00:21:53.301325+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22765","created_at":"2026-07-28T00:21:53.301325+00:00"},{"alias_kind":"pith_short_12","alias_value":"PEGAVCCHZAMZ","created_at":"2026-07-28T00:21:53.301325+00:00"},{"alias_kind":"pith_short_16","alias_value":"PEGAVCCHZAMZRSHX","created_at":"2026-07-28T00:21:53.301325+00:00"},{"alias_kind":"pith_short_8","alias_value":"PEGAVCCH","created_at":"2026-07-28T00:21:53.301325+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PEGAVCCHZAMZRSHX5BE543H7ZX","json":"https://pith.science/pith/PEGAVCCHZAMZRSHX5BE543H7ZX.json","graph_json":"https://pith.science/api/pith-number/PEGAVCCHZAMZRSHX5BE543H7ZX/graph.json","events_json":"https://pith.science/api/pith-number/PEGAVCCHZAMZRSHX5BE543H7ZX/events.json","paper":"https://pith.science/paper/PEGAVCCH"},"agent_actions":{"view_html":"https://pith.science/pith/PEGAVCCHZAMZRSHX5BE543H7ZX","download_json":"https://pith.science/pith/PEGAVCCHZAMZRSHX5BE543H7ZX.json","view_paper":"https://pith.science/paper/PEGAVCCH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.22765&json=true","fetch_graph":"https://pith.science/api/pith-number/PEGAVCCHZAMZRSHX5BE543H7ZX/graph.json","fetch_events":"https://pith.science/api/pith-number/PEGAVCCHZAMZRSHX5BE543H7ZX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PEGAVCCHZAMZRSHX5BE543H7ZX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PEGAVCCHZAMZRSHX5BE543H7ZX/action/storage_attestation","attest_author":"https://pith.science/pith/PEGAVCCHZAMZRSHX5BE543H7ZX/action/author_attestation","sign_citation":"https://pith.science/pith/PEGAVCCHZAMZRSHX5BE543H7ZX/action/citation_signature","submit_replication":"https://pith.science/pith/PEGAVCCHZAMZRSHX5BE543H7ZX/action/replication_record"}},"created_at":"2026-07-28T00:21:53.301325+00:00","updated_at":"2026-07-28T00:21:53.301325+00:00"}