{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:B2HHZNBZZBNRE27EWJEI2DEWUL","short_pith_number":"pith:B2HHZNBZ","schema_version":"1.0","canonical_sha256":"0e8e7cb439c85b126be4b2488d0c96a2cdf7b9a95b57d5acc58e56b857b06fc5","source":{"kind":"arxiv","id":"2606.30012","version":1},"attestation_state":"computed","paper":{"title":"SkelEM: Training-Signal Decoupling of Skeleton and Diffusion for Self-supervised Axial Super-Resolution in Volume Microscopy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bohao Chen, Chenxun Deng, Hua Han, Xi Chen, Yanan Lv, Yanchao Zhang","submitted_at":"2026-06-29T09:19:04Z","abstract_excerpt":"Volume microscopy, including electron and light microscopy, suffers from severe anisotropic resolution due to physical axial sectioning. Existing self-supervised axial super-resolution (ASR) methods face a trilemma bounded by overly smoothed regression textures, structural hallucinations of pure diffusion models, and prohibitive inference latency. In this paper, we propose Skeleton-refinE Microscopy (SkelEM), a self-supervised framework that decouples ASR at the training-signal level: a frozen topological network and a diffusion refiner are optimized by disjoint objectives, separating low-freq"},"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":"2606.30012","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-29T09:19:04Z","cross_cats_sorted":[],"title_canon_sha256":"896e52d91093a5c247a3b7198c09a72f9495f3e7f77ce2fc93431d7e0fe63008","abstract_canon_sha256":"322c66f31cae0f5fb47813e50b250f139cf0426f66aad19725ffd0f0683b5346"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:17:45.717445Z","signature_b64":"a4FCo4a4YAC2AzMfnxGM//JX0rNg4tH86Gk1Hc5+JM9DVjojATVj+2xxpT+s6YR2Kxybrhk19K00h3vmgRnRBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e8e7cb439c85b126be4b2488d0c96a2cdf7b9a95b57d5acc58e56b857b06fc5","last_reissued_at":"2026-06-30T02:17:45.716900Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:17:45.716900Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SkelEM: Training-Signal Decoupling of Skeleton and Diffusion for Self-supervised Axial Super-Resolution in Volume Microscopy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bohao Chen, Chenxun Deng, Hua Han, Xi Chen, Yanan Lv, Yanchao Zhang","submitted_at":"2026-06-29T09:19:04Z","abstract_excerpt":"Volume microscopy, including electron and light microscopy, suffers from severe anisotropic resolution due to physical axial sectioning. Existing self-supervised axial super-resolution (ASR) methods face a trilemma bounded by overly smoothed regression textures, structural hallucinations of pure diffusion models, and prohibitive inference latency. In this paper, we propose Skeleton-refinE Microscopy (SkelEM), a self-supervised framework that decouples ASR at the training-signal level: a frozen topological network and a diffusion refiner are optimized by disjoint objectives, separating low-freq"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30012","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/2606.30012/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":"2606.30012","created_at":"2026-06-30T02:17:45.716996+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.30012v1","created_at":"2026-06-30T02:17:45.716996+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30012","created_at":"2026-06-30T02:17:45.716996+00:00"},{"alias_kind":"pith_short_12","alias_value":"B2HHZNBZZBNR","created_at":"2026-06-30T02:17:45.716996+00:00"},{"alias_kind":"pith_short_16","alias_value":"B2HHZNBZZBNRE27E","created_at":"2026-06-30T02:17:45.716996+00:00"},{"alias_kind":"pith_short_8","alias_value":"B2HHZNBZ","created_at":"2026-06-30T02:17:45.716996+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/B2HHZNBZZBNRE27EWJEI2DEWUL","json":"https://pith.science/pith/B2HHZNBZZBNRE27EWJEI2DEWUL.json","graph_json":"https://pith.science/api/pith-number/B2HHZNBZZBNRE27EWJEI2DEWUL/graph.json","events_json":"https://pith.science/api/pith-number/B2HHZNBZZBNRE27EWJEI2DEWUL/events.json","paper":"https://pith.science/paper/B2HHZNBZ"},"agent_actions":{"view_html":"https://pith.science/pith/B2HHZNBZZBNRE27EWJEI2DEWUL","download_json":"https://pith.science/pith/B2HHZNBZZBNRE27EWJEI2DEWUL.json","view_paper":"https://pith.science/paper/B2HHZNBZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.30012&json=true","fetch_graph":"https://pith.science/api/pith-number/B2HHZNBZZBNRE27EWJEI2DEWUL/graph.json","fetch_events":"https://pith.science/api/pith-number/B2HHZNBZZBNRE27EWJEI2DEWUL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B2HHZNBZZBNRE27EWJEI2DEWUL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B2HHZNBZZBNRE27EWJEI2DEWUL/action/storage_attestation","attest_author":"https://pith.science/pith/B2HHZNBZZBNRE27EWJEI2DEWUL/action/author_attestation","sign_citation":"https://pith.science/pith/B2HHZNBZZBNRE27EWJEI2DEWUL/action/citation_signature","submit_replication":"https://pith.science/pith/B2HHZNBZZBNRE27EWJEI2DEWUL/action/replication_record"}},"created_at":"2026-06-30T02:17:45.716996+00:00","updated_at":"2026-06-30T02:17:45.716996+00:00"}