{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:K7O3BBI7ZO52AMKKBR7IHA5LTW","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":"a60c950cad91f6dcd33bd41c410b484976283d226c62d100cbfed77d4c38b597","cross_cats_sorted":["eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-17T03:05:13Z","title_canon_sha256":"1da40b6cb6f7d78b5e69f2fdc0d95a5db4df588d955c43d69feef567243f7726"},"schema_version":"1.0","source":{"id":"2505.11800","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.11800","created_at":"2026-07-05T11:04:52Z"},{"alias_kind":"arxiv_version","alias_value":"2505.11800v1","created_at":"2026-07-05T11:04:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.11800","created_at":"2026-07-05T11:04:52Z"},{"alias_kind":"pith_short_12","alias_value":"K7O3BBI7ZO52","created_at":"2026-07-05T11:04:52Z"},{"alias_kind":"pith_short_16","alias_value":"K7O3BBI7ZO52AMKK","created_at":"2026-07-05T11:04:52Z"},{"alias_kind":"pith_short_8","alias_value":"K7O3BBI7","created_at":"2026-07-05T11:04:52Z"}],"graph_snapshots":[{"event_id":"sha256:c5b5d9aab44974d7e54b5859c4088a59ee64dbc3c77d8a781e9e784b4b32f346","target":"graph","created_at":"2026-07-05T11:04:52Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.11800/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hyperspectral and multispectral image (HSI-MSI) fusion involves combining a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to generate a high-resolution hyperspectral image (HR-HSI). Most deep learning-based methods for HSI-MSI fusion rely on large amounts of hyperspectral data for supervised training, which is often scarce in practical applications. In this paper, we propose a self-learning Adaptive Residual Guided Subspace Diffusion Model (ARGS-Diff), which only utilizes the observed images without any extra training data. Specifically, as the","authors_text":"He Wang, Jian Zhu, Yang Xu, Zebin Wu, Zhihui Wei","cross_cats":["eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-17T03:05:13Z","title":"Self-Learning Hyperspectral and Multispectral Image Fusion via Adaptive Residual Guided Subspace Diffusion Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.11800","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":"sha256:155c2006306a322481268bd417f520dbceb1881ecc1d4ec54c1fc598ac470ec4","target":"record","created_at":"2026-07-05T11:04:52Z","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":"a60c950cad91f6dcd33bd41c410b484976283d226c62d100cbfed77d4c38b597","cross_cats_sorted":["eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-17T03:05:13Z","title_canon_sha256":"1da40b6cb6f7d78b5e69f2fdc0d95a5db4df588d955c43d69feef567243f7726"},"schema_version":"1.0","source":{"id":"2505.11800","kind":"arxiv","version":1}},"canonical_sha256":"57ddb0851fcbbba0314a0c7e8383ab9d8fa7fffda72299132b506625c111ba35","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"57ddb0851fcbbba0314a0c7e8383ab9d8fa7fffda72299132b506625c111ba35","first_computed_at":"2026-07-05T11:04:52.887907Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:04:52.887907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZEZsIhqh263SZTaB1/XDaNJXyOIFdH8JtbqXRE0+xk0K6RTY60eVWZiITyCfeS9EGVju7cB2IirqWf6rX9OzDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:04:52.888426Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.11800","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:155c2006306a322481268bd417f520dbceb1881ecc1d4ec54c1fc598ac470ec4","sha256:c5b5d9aab44974d7e54b5859c4088a59ee64dbc3c77d8a781e9e784b4b32f346"],"state_sha256":"6e44fa9e45913a57b6d74305dd0895dcd1e5b496e4e1a97953c6fac2c5178754"}