{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7FYPVQDPQ4SHFRGFZQWPUFYI36","short_pith_number":"pith:7FYPVQDP","schema_version":"1.0","canonical_sha256":"f970fac06f872472c4c5cc2cfa1708df914aa4d2959d653b31a33c2eed0425ce","source":{"kind":"arxiv","id":"2504.19289","version":1},"attestation_state":"computed","paper":{"title":"Marine Snow Removal Using Internally Generated Pseudo Ground Truth","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexandra Malyugina, Benjamin Leslie, Eduardo Ruiz, Guoxi Huang, Nantheera Anantrasirichai","submitted_at":"2025-04-27T16:08:00Z","abstract_excerpt":"Underwater videos often suffer from degraded quality due to light absorption, scattering, and various noise sources. Among these, marine snow, which is suspended organic particles appearing as bright spots or noise, significantly impacts machine vision tasks, particularly those involving feature matching. Existing methods for removing marine snow are ineffective due to the lack of paired training data. To address this challenge, this paper proposes a novel enhancement framework that introduces a new approach for generating paired datasets from raw underwater videos. The resulting dataset consi"},"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":"2504.19289","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-27T16:08:00Z","cross_cats_sorted":[],"title_canon_sha256":"421fa6bbd45cdfd534cdd8074b12ba179d09a3f5c917834ea5008efd89068c3a","abstract_canon_sha256":"23680c2d1c1a420d28d5d115d3e9aef0a90f2bd28e9ef983ea05c9e1f91be842"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:54:55.196074Z","signature_b64":"GQtE9rKF/mOupdoTAzS7L1NBf650RTMJGbZo92NVSu6MAOShu7G+++UrXMPd1ezjBCmsgKLzZiY2oPURMdSRDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f970fac06f872472c4c5cc2cfa1708df914aa4d2959d653b31a33c2eed0425ce","last_reissued_at":"2026-07-05T10:54:55.195606Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:54:55.195606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Marine Snow Removal Using Internally Generated Pseudo Ground Truth","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexandra Malyugina, Benjamin Leslie, Eduardo Ruiz, Guoxi Huang, Nantheera Anantrasirichai","submitted_at":"2025-04-27T16:08:00Z","abstract_excerpt":"Underwater videos often suffer from degraded quality due to light absorption, scattering, and various noise sources. Among these, marine snow, which is suspended organic particles appearing as bright spots or noise, significantly impacts machine vision tasks, particularly those involving feature matching. Existing methods for removing marine snow are ineffective due to the lack of paired training data. To address this challenge, this paper proposes a novel enhancement framework that introduces a new approach for generating paired datasets from raw underwater videos. The resulting dataset consi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.19289","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/2504.19289/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":"2504.19289","created_at":"2026-07-05T10:54:55.195664+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.19289v1","created_at":"2026-07-05T10:54:55.195664+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.19289","created_at":"2026-07-05T10:54:55.195664+00:00"},{"alias_kind":"pith_short_12","alias_value":"7FYPVQDPQ4SH","created_at":"2026-07-05T10:54:55.195664+00:00"},{"alias_kind":"pith_short_16","alias_value":"7FYPVQDPQ4SHFRGF","created_at":"2026-07-05T10:54:55.195664+00:00"},{"alias_kind":"pith_short_8","alias_value":"7FYPVQDP","created_at":"2026-07-05T10:54:55.195664+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.14265","citing_title":"Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement","ref_index":38,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7FYPVQDPQ4SHFRGFZQWPUFYI36","json":"https://pith.science/pith/7FYPVQDPQ4SHFRGFZQWPUFYI36.json","graph_json":"https://pith.science/api/pith-number/7FYPVQDPQ4SHFRGFZQWPUFYI36/graph.json","events_json":"https://pith.science/api/pith-number/7FYPVQDPQ4SHFRGFZQWPUFYI36/events.json","paper":"https://pith.science/paper/7FYPVQDP"},"agent_actions":{"view_html":"https://pith.science/pith/7FYPVQDPQ4SHFRGFZQWPUFYI36","download_json":"https://pith.science/pith/7FYPVQDPQ4SHFRGFZQWPUFYI36.json","view_paper":"https://pith.science/paper/7FYPVQDP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.19289&json=true","fetch_graph":"https://pith.science/api/pith-number/7FYPVQDPQ4SHFRGFZQWPUFYI36/graph.json","fetch_events":"https://pith.science/api/pith-number/7FYPVQDPQ4SHFRGFZQWPUFYI36/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7FYPVQDPQ4SHFRGFZQWPUFYI36/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7FYPVQDPQ4SHFRGFZQWPUFYI36/action/storage_attestation","attest_author":"https://pith.science/pith/7FYPVQDPQ4SHFRGFZQWPUFYI36/action/author_attestation","sign_citation":"https://pith.science/pith/7FYPVQDPQ4SHFRGFZQWPUFYI36/action/citation_signature","submit_replication":"https://pith.science/pith/7FYPVQDPQ4SHFRGFZQWPUFYI36/action/replication_record"}},"created_at":"2026-07-05T10:54:55.195664+00:00","updated_at":"2026-07-05T10:54:55.195664+00:00"}