{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BKJNXE7W2INZWXS26MLECB7EBS","short_pith_number":"pith:BKJNXE7W","schema_version":"1.0","canonical_sha256":"0a92db93f6d21b9b5e5af3164107e40c8fe1e5f1381dafe2d03d2697e5d727ad","source":{"kind":"arxiv","id":"2504.03136","version":1},"attestation_state":"computed","paper":{"title":"Classic Video Denoising in a Machine Learning World: Robust, Fast, and Controllable","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chongyi Li, Chunle Guo, Jiawen Chen, Simon Niklaus, Xin Jin, Yuting Yang, Zhihao Xia, Zhoutong Zhang","submitted_at":"2025-04-04T03:03:23Z","abstract_excerpt":"Denoising is a crucial step in many video processing pipelines such as in interactive editing, where high quality, speed, and user control are essential. While recent approaches achieve significant improvements in denoising quality by leveraging deep learning, they are prone to unexpected failures due to discrepancies between training data distributions and the wide variety of noise patterns found in real-world videos. These methods also tend to be slow and lack user control. In contrast, traditional denoising methods perform reliably on in-the-wild videos and run relatively quickly on modern "},"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":"2504.03136","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-04T03:03:23Z","cross_cats_sorted":[],"title_canon_sha256":"3915143d5f25e3f1bc145377e969ccf33780cb6dbb2804cbe2a2260e1f353a77","abstract_canon_sha256":"28179b663d9d2d4e91e944db41409a0aa1b65c6317d79fea731b13691ff3e101"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:44:26.917713Z","signature_b64":"oNiu4QurFRCMiw29BDs0KV1WYO24pEhkFHvjJKsLc2RR7IEm3ZP9EqSM8cTKy4c3xfJUJlgRrtifbzQBIUmLAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a92db93f6d21b9b5e5af3164107e40c8fe1e5f1381dafe2d03d2697e5d727ad","last_reissued_at":"2026-07-05T10:44:26.917295Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:44:26.917295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Classic Video Denoising in a Machine Learning World: Robust, Fast, and Controllable","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chongyi Li, Chunle Guo, Jiawen Chen, Simon Niklaus, Xin Jin, Yuting Yang, Zhihao Xia, Zhoutong Zhang","submitted_at":"2025-04-04T03:03:23Z","abstract_excerpt":"Denoising is a crucial step in many video processing pipelines such as in interactive editing, where high quality, speed, and user control are essential. While recent approaches achieve significant improvements in denoising quality by leveraging deep learning, they are prone to unexpected failures due to discrepancies between training data distributions and the wide variety of noise patterns found in real-world videos. These methods also tend to be slow and lack user control. In contrast, traditional denoising methods perform reliably on in-the-wild videos and run relatively quickly on modern "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.03136","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/2504.03136/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":"2504.03136","created_at":"2026-07-05T10:44:26.917363+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.03136v1","created_at":"2026-07-05T10:44:26.917363+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.03136","created_at":"2026-07-05T10:44:26.917363+00:00"},{"alias_kind":"pith_short_12","alias_value":"BKJNXE7W2INZ","created_at":"2026-07-05T10:44:26.917363+00:00"},{"alias_kind":"pith_short_16","alias_value":"BKJNXE7W2INZWXS2","created_at":"2026-07-05T10:44:26.917363+00:00"},{"alias_kind":"pith_short_8","alias_value":"BKJNXE7W","created_at":"2026-07-05T10:44:26.917363+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/BKJNXE7W2INZWXS26MLECB7EBS","json":"https://pith.science/pith/BKJNXE7W2INZWXS26MLECB7EBS.json","graph_json":"https://pith.science/api/pith-number/BKJNXE7W2INZWXS26MLECB7EBS/graph.json","events_json":"https://pith.science/api/pith-number/BKJNXE7W2INZWXS26MLECB7EBS/events.json","paper":"https://pith.science/paper/BKJNXE7W"},"agent_actions":{"view_html":"https://pith.science/pith/BKJNXE7W2INZWXS26MLECB7EBS","download_json":"https://pith.science/pith/BKJNXE7W2INZWXS26MLECB7EBS.json","view_paper":"https://pith.science/paper/BKJNXE7W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.03136&json=true","fetch_graph":"https://pith.science/api/pith-number/BKJNXE7W2INZWXS26MLECB7EBS/graph.json","fetch_events":"https://pith.science/api/pith-number/BKJNXE7W2INZWXS26MLECB7EBS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BKJNXE7W2INZWXS26MLECB7EBS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BKJNXE7W2INZWXS26MLECB7EBS/action/storage_attestation","attest_author":"https://pith.science/pith/BKJNXE7W2INZWXS26MLECB7EBS/action/author_attestation","sign_citation":"https://pith.science/pith/BKJNXE7W2INZWXS26MLECB7EBS/action/citation_signature","submit_replication":"https://pith.science/pith/BKJNXE7W2INZWXS26MLECB7EBS/action/replication_record"}},"created_at":"2026-07-05T10:44:26.917363+00:00","updated_at":"2026-07-05T10:44:26.917363+00:00"}