{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:COXPHQ4F3D2C2HS5O5D3U5QMGT","short_pith_number":"pith:COXPHQ4F","schema_version":"1.0","canonical_sha256":"13aef3c385d8f42d1e5d7747ba760c34d46c68754b3cade4f5b0cc9945c15f6b","source":{"kind":"arxiv","id":"2303.06682","version":2},"attestation_state":"computed","paper":{"title":"DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Dacheng Tao, Lefei Zhang, Liangpei Zhang, Yuchun Miao","submitted_at":"2023-03-12T14:57:04Z","abstract_excerpt":"Diffusion models have recently received a surge of interest due to their impressive performance for image restoration, especially in terms of noise robustness. However, existing diffusion-based methods are trained on a large amount of training data and perform very well in-distribution, but can be quite susceptible to distribution shift. This is especially inappropriate for data-starved hyperspectral image (HSI) restoration. To tackle this problem, this work puts forth a self-supervised diffusion model for HSI restoration, namely Denoising Diffusion Spatio-Spectral Model (\\texttt{DDS2M}), whic"},"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":"2303.06682","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-12T14:57:04Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"07e6aa70ceae4ea0132157f6cf258ddfe72fb2bcdb6344f5751b0b64bfd2d9ea","abstract_canon_sha256":"5c7d12517b6e39b711aa5f42b24d33b179c77f490c08d5547e511ac54a01ff03"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:52:27.878254Z","signature_b64":"vxez9m+8O1U88bUOQ2f59MYepMmRDI74LZpj0vp4m3umMDjoQt+Tti3GVY2E6n/KRex6LN1E6E2aGsnRKon1BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13aef3c385d8f42d1e5d7747ba760c34d46c68754b3cade4f5b0cc9945c15f6b","last_reissued_at":"2026-07-05T05:52:27.877772Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:52:27.877772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Dacheng Tao, Lefei Zhang, Liangpei Zhang, Yuchun Miao","submitted_at":"2023-03-12T14:57:04Z","abstract_excerpt":"Diffusion models have recently received a surge of interest due to their impressive performance for image restoration, especially in terms of noise robustness. However, existing diffusion-based methods are trained on a large amount of training data and perform very well in-distribution, but can be quite susceptible to distribution shift. This is especially inappropriate for data-starved hyperspectral image (HSI) restoration. To tackle this problem, this work puts forth a self-supervised diffusion model for HSI restoration, namely Denoising Diffusion Spatio-Spectral Model (\\texttt{DDS2M}), whic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.06682","kind":"arxiv","version":2},"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/2303.06682/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":"2303.06682","created_at":"2026-07-05T05:52:27.877840+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.06682v2","created_at":"2026-07-05T05:52:27.877840+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.06682","created_at":"2026-07-05T05:52:27.877840+00:00"},{"alias_kind":"pith_short_12","alias_value":"COXPHQ4F3D2C","created_at":"2026-07-05T05:52:27.877840+00:00"},{"alias_kind":"pith_short_16","alias_value":"COXPHQ4F3D2C2HS5","created_at":"2026-07-05T05:52:27.877840+00:00"},{"alias_kind":"pith_short_8","alias_value":"COXPHQ4F","created_at":"2026-07-05T05:52:27.877840+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.11085","citing_title":"Atmos-Bench: 3D Atmospheric Structures for Climate Insight","ref_index":45,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/COXPHQ4F3D2C2HS5O5D3U5QMGT","json":"https://pith.science/pith/COXPHQ4F3D2C2HS5O5D3U5QMGT.json","graph_json":"https://pith.science/api/pith-number/COXPHQ4F3D2C2HS5O5D3U5QMGT/graph.json","events_json":"https://pith.science/api/pith-number/COXPHQ4F3D2C2HS5O5D3U5QMGT/events.json","paper":"https://pith.science/paper/COXPHQ4F"},"agent_actions":{"view_html":"https://pith.science/pith/COXPHQ4F3D2C2HS5O5D3U5QMGT","download_json":"https://pith.science/pith/COXPHQ4F3D2C2HS5O5D3U5QMGT.json","view_paper":"https://pith.science/paper/COXPHQ4F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.06682&json=true","fetch_graph":"https://pith.science/api/pith-number/COXPHQ4F3D2C2HS5O5D3U5QMGT/graph.json","fetch_events":"https://pith.science/api/pith-number/COXPHQ4F3D2C2HS5O5D3U5QMGT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/COXPHQ4F3D2C2HS5O5D3U5QMGT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/COXPHQ4F3D2C2HS5O5D3U5QMGT/action/storage_attestation","attest_author":"https://pith.science/pith/COXPHQ4F3D2C2HS5O5D3U5QMGT/action/author_attestation","sign_citation":"https://pith.science/pith/COXPHQ4F3D2C2HS5O5D3U5QMGT/action/citation_signature","submit_replication":"https://pith.science/pith/COXPHQ4F3D2C2HS5O5D3U5QMGT/action/replication_record"}},"created_at":"2026-07-05T05:52:27.877840+00:00","updated_at":"2026-07-05T05:52:27.877840+00:00"}