{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GT6WWSZHJJ3Z2GW4UTG623PCME","short_pith_number":"pith:GT6WWSZH","schema_version":"1.0","canonical_sha256":"34fd6b4b274a779d1adca4cded6de26111c4894c17e7d0aa72fa5abf58b80f07","source":{"kind":"arxiv","id":"2412.10338","version":3},"attestation_state":"computed","paper":{"title":"XYScanNet: A State Space Model for Single Image Deblurring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengkai Liu, Hanzhou Liu, Jiacong Xu, Mi Lu, Peng Jiang","submitted_at":"2024-12-13T18:33:18Z","abstract_excerpt":"Deep state-space models (SSMs), like recent Mamba architectures, are emerging as a promising alternative to CNN and Transformer networks. Existing Mamba-based restoration methods process visual data by leveraging a flatten-and-scan strategy that converts image patches into a 1D sequence before scanning. However, this scanning paradigm ignores local pixel dependencies and introduces spatial misalignment by positioning distant pixels incorrectly adjacent, which reduces local noise-awareness and degrades image sharpness in low-level vision tasks. To overcome these issues, we propose a novel slice"},"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":"2412.10338","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-13T18:33:18Z","cross_cats_sorted":[],"title_canon_sha256":"1b3917f586ff04524d479cdc510d12957e823447c3642f575e4d220030d527ba","abstract_canon_sha256":"bf495c702e18fe9254cf549f92d67b96f310f3098ef290c66d8e6a670b128ad7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:06.355809Z","signature_b64":"eP2BURvJHQK0iEFNxa5eRFtjkrvJXuFitJGSWw8RYFe8vbojr2pt49dtgkznghvuZvTlRo1MUjmhEim5GNcMDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34fd6b4b274a779d1adca4cded6de26111c4894c17e7d0aa72fa5abf58b80f07","last_reissued_at":"2026-07-05T11:25:06.355333Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:06.355333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"XYScanNet: A State Space Model for Single Image Deblurring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengkai Liu, Hanzhou Liu, Jiacong Xu, Mi Lu, Peng Jiang","submitted_at":"2024-12-13T18:33:18Z","abstract_excerpt":"Deep state-space models (SSMs), like recent Mamba architectures, are emerging as a promising alternative to CNN and Transformer networks. Existing Mamba-based restoration methods process visual data by leveraging a flatten-and-scan strategy that converts image patches into a 1D sequence before scanning. However, this scanning paradigm ignores local pixel dependencies and introduces spatial misalignment by positioning distant pixels incorrectly adjacent, which reduces local noise-awareness and degrades image sharpness in low-level vision tasks. To overcome these issues, we propose a novel slice"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.10338","kind":"arxiv","version":3},"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/2412.10338/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":"2412.10338","created_at":"2026-07-05T11:25:06.355395+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.10338v3","created_at":"2026-07-05T11:25:06.355395+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.10338","created_at":"2026-07-05T11:25:06.355395+00:00"},{"alias_kind":"pith_short_12","alias_value":"GT6WWSZHJJ3Z","created_at":"2026-07-05T11:25:06.355395+00:00"},{"alias_kind":"pith_short_16","alias_value":"GT6WWSZHJJ3Z2GW4","created_at":"2026-07-05T11:25:06.355395+00:00"},{"alias_kind":"pith_short_8","alias_value":"GT6WWSZH","created_at":"2026-07-05T11:25:06.355395+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/GT6WWSZHJJ3Z2GW4UTG623PCME","json":"https://pith.science/pith/GT6WWSZHJJ3Z2GW4UTG623PCME.json","graph_json":"https://pith.science/api/pith-number/GT6WWSZHJJ3Z2GW4UTG623PCME/graph.json","events_json":"https://pith.science/api/pith-number/GT6WWSZHJJ3Z2GW4UTG623PCME/events.json","paper":"https://pith.science/paper/GT6WWSZH"},"agent_actions":{"view_html":"https://pith.science/pith/GT6WWSZHJJ3Z2GW4UTG623PCME","download_json":"https://pith.science/pith/GT6WWSZHJJ3Z2GW4UTG623PCME.json","view_paper":"https://pith.science/paper/GT6WWSZH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.10338&json=true","fetch_graph":"https://pith.science/api/pith-number/GT6WWSZHJJ3Z2GW4UTG623PCME/graph.json","fetch_events":"https://pith.science/api/pith-number/GT6WWSZHJJ3Z2GW4UTG623PCME/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GT6WWSZHJJ3Z2GW4UTG623PCME/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GT6WWSZHJJ3Z2GW4UTG623PCME/action/storage_attestation","attest_author":"https://pith.science/pith/GT6WWSZHJJ3Z2GW4UTG623PCME/action/author_attestation","sign_citation":"https://pith.science/pith/GT6WWSZHJJ3Z2GW4UTG623PCME/action/citation_signature","submit_replication":"https://pith.science/pith/GT6WWSZHJJ3Z2GW4UTG623PCME/action/replication_record"}},"created_at":"2026-07-05T11:25:06.355395+00:00","updated_at":"2026-07-05T11:25:06.355395+00:00"}