{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KUMGBCCEUX5X54FMJB6SAOI523","short_pith_number":"pith:KUMGBCCE","schema_version":"1.0","canonical_sha256":"5518608844a5fb7ef0ac487d20391dd6ee5b66fa201edc9d5ad58e07b410efdb","source":{"kind":"arxiv","id":"2408.11758","version":2},"attestation_state":"computed","paper":{"title":"MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingchen Li, Mengxi Guo, Shijie Zhao, Xin Li, Yulin Ren, Zhibo Chen","submitted_at":"2024-08-21T16:30:45Z","abstract_excerpt":"We present MambaCSR, a simple but effective framework based on Mamba for the challenging compressed image super-resolution (CSR) task. Particularly, the scanning strategies of Mamba are crucial for effective contextual knowledge modeling in the restoration process despite it relying on selective state space modeling for all tokens. In this work, we propose an efficient dual-interleaved scanning paradigm (DIS) for CSR, which is composed of two scanning strategies: (i) hierarchical interleaved scanning is designed to comprehensively capture and utilize the most potential contextual information w"},"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":"2408.11758","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-21T16:30:45Z","cross_cats_sorted":[],"title_canon_sha256":"16192e1194c674fb4006cf628def03378ac0d5ba5b2d631ac5e380fbec241c94","abstract_canon_sha256":"fbad74bad3ec0b4239747f145214b9ea35424bec38c317d070fcd387ad556d90"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:19.250876Z","signature_b64":"opC+F1gyGcjxmi+762hEYxH5rT+T/Rs6IdKJ0WHmeIg4f6ECqPc6J4ADxdBVEvtkWXyBcyTz9f5lOYwoTKysDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5518608844a5fb7ef0ac487d20391dd6ee5b66fa201edc9d5ad58e07b410efdb","last_reissued_at":"2026-07-05T09:40:19.250401Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:19.250401Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingchen Li, Mengxi Guo, Shijie Zhao, Xin Li, Yulin Ren, Zhibo Chen","submitted_at":"2024-08-21T16:30:45Z","abstract_excerpt":"We present MambaCSR, a simple but effective framework based on Mamba for the challenging compressed image super-resolution (CSR) task. Particularly, the scanning strategies of Mamba are crucial for effective contextual knowledge modeling in the restoration process despite it relying on selective state space modeling for all tokens. In this work, we propose an efficient dual-interleaved scanning paradigm (DIS) for CSR, which is composed of two scanning strategies: (i) hierarchical interleaved scanning is designed to comprehensively capture and utilize the most potential contextual information w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.11758","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/2408.11758/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":"2408.11758","created_at":"2026-07-05T09:40:19.250472+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.11758v2","created_at":"2026-07-05T09:40:19.250472+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.11758","created_at":"2026-07-05T09:40:19.250472+00:00"},{"alias_kind":"pith_short_12","alias_value":"KUMGBCCEUX5X","created_at":"2026-07-05T09:40:19.250472+00:00"},{"alias_kind":"pith_short_16","alias_value":"KUMGBCCEUX5X54FM","created_at":"2026-07-05T09:40:19.250472+00:00"},{"alias_kind":"pith_short_8","alias_value":"KUMGBCCE","created_at":"2026-07-05T09:40:19.250472+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25329","citing_title":"State Space Models Meet Remote Sensing: A Survey","ref_index":119,"is_internal_anchor":false},{"citing_arxiv_id":"2601.17723","citing_title":"Implicit Neural Representation-Based Continuous Single Image Super-Resolution: An Empirical Benchmark","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KUMGBCCEUX5X54FMJB6SAOI523","json":"https://pith.science/pith/KUMGBCCEUX5X54FMJB6SAOI523.json","graph_json":"https://pith.science/api/pith-number/KUMGBCCEUX5X54FMJB6SAOI523/graph.json","events_json":"https://pith.science/api/pith-number/KUMGBCCEUX5X54FMJB6SAOI523/events.json","paper":"https://pith.science/paper/KUMGBCCE"},"agent_actions":{"view_html":"https://pith.science/pith/KUMGBCCEUX5X54FMJB6SAOI523","download_json":"https://pith.science/pith/KUMGBCCEUX5X54FMJB6SAOI523.json","view_paper":"https://pith.science/paper/KUMGBCCE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.11758&json=true","fetch_graph":"https://pith.science/api/pith-number/KUMGBCCEUX5X54FMJB6SAOI523/graph.json","fetch_events":"https://pith.science/api/pith-number/KUMGBCCEUX5X54FMJB6SAOI523/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KUMGBCCEUX5X54FMJB6SAOI523/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KUMGBCCEUX5X54FMJB6SAOI523/action/storage_attestation","attest_author":"https://pith.science/pith/KUMGBCCEUX5X54FMJB6SAOI523/action/author_attestation","sign_citation":"https://pith.science/pith/KUMGBCCEUX5X54FMJB6SAOI523/action/citation_signature","submit_replication":"https://pith.science/pith/KUMGBCCEUX5X54FMJB6SAOI523/action/replication_record"}},"created_at":"2026-07-05T09:40:19.250472+00:00","updated_at":"2026-07-05T09:40:19.250472+00:00"}