{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QB7BCAN47K2UDWRE6AOK5MJUQO","short_pith_number":"pith:QB7BCAN4","schema_version":"1.0","canonical_sha256":"807e1101bcfab541da24f01caeb13483b007ada3c1cdd5aa93b6a25b6960d7ff","source":{"kind":"arxiv","id":"1909.06148","version":1},"attestation_state":"computed","paper":{"title":"Video Rain/Snow Removal by Transformed Online Multiscale Convolutional Sparse Coding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Chenqiang Gao, Deyu Meng, Lei Zhang, Minghan Li, Qian Zhao, Xiangyong Cao","submitted_at":"2019-09-13T11:22:29Z","abstract_excerpt":"Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time with the background scene transformed occasionally. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of bo"},"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":"1909.06148","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-13T11:22:29Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"b87d509eceefc94f2c25db463eaf241928fd8a9cf607b286cf5ec2c419437350","abstract_canon_sha256":"4f69c3b0277bd31012fa6a23e2065273d7f3f971fd4ba07e55038da6c8d210bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:04:33.055882Z","signature_b64":"PWvfo+fdZ3Cu4aS+1hQqNKyd6ehR/QsgwTILOAK+t01/1oqwn32Mo2k8wCh1eIJ/PgD/JdGeG5DqZo/hhB6eCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"807e1101bcfab541da24f01caeb13483b007ada3c1cdd5aa93b6a25b6960d7ff","last_reissued_at":"2026-07-05T00:04:33.055413Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:04:33.055413Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Video Rain/Snow Removal by Transformed Online Multiscale Convolutional Sparse Coding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Chenqiang Gao, Deyu Meng, Lei Zhang, Minghan Li, Qian Zhao, Xiangyong Cao","submitted_at":"2019-09-13T11:22:29Z","abstract_excerpt":"Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time with the background scene transformed occasionally. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of bo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.06148","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/1909.06148/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":"1909.06148","created_at":"2026-07-05T00:04:33.055470+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.06148v1","created_at":"2026-07-05T00:04:33.055470+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.06148","created_at":"2026-07-05T00:04:33.055470+00:00"},{"alias_kind":"pith_short_12","alias_value":"QB7BCAN47K2U","created_at":"2026-07-05T00:04:33.055470+00:00"},{"alias_kind":"pith_short_16","alias_value":"QB7BCAN47K2UDWRE","created_at":"2026-07-05T00:04:33.055470+00:00"},{"alias_kind":"pith_short_8","alias_value":"QB7BCAN4","created_at":"2026-07-05T00:04:33.055470+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.20901","citing_title":"Event-Based De-Snowing for Autonomous Driving","ref_index":13,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QB7BCAN47K2UDWRE6AOK5MJUQO","json":"https://pith.science/pith/QB7BCAN47K2UDWRE6AOK5MJUQO.json","graph_json":"https://pith.science/api/pith-number/QB7BCAN47K2UDWRE6AOK5MJUQO/graph.json","events_json":"https://pith.science/api/pith-number/QB7BCAN47K2UDWRE6AOK5MJUQO/events.json","paper":"https://pith.science/paper/QB7BCAN4"},"agent_actions":{"view_html":"https://pith.science/pith/QB7BCAN47K2UDWRE6AOK5MJUQO","download_json":"https://pith.science/pith/QB7BCAN47K2UDWRE6AOK5MJUQO.json","view_paper":"https://pith.science/paper/QB7BCAN4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.06148&json=true","fetch_graph":"https://pith.science/api/pith-number/QB7BCAN47K2UDWRE6AOK5MJUQO/graph.json","fetch_events":"https://pith.science/api/pith-number/QB7BCAN47K2UDWRE6AOK5MJUQO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QB7BCAN47K2UDWRE6AOK5MJUQO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QB7BCAN47K2UDWRE6AOK5MJUQO/action/storage_attestation","attest_author":"https://pith.science/pith/QB7BCAN47K2UDWRE6AOK5MJUQO/action/author_attestation","sign_citation":"https://pith.science/pith/QB7BCAN47K2UDWRE6AOK5MJUQO/action/citation_signature","submit_replication":"https://pith.science/pith/QB7BCAN47K2UDWRE6AOK5MJUQO/action/replication_record"}},"created_at":"2026-07-05T00:04:33.055470+00:00","updated_at":"2026-07-05T00:04:33.055470+00:00"}