{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:OPMT4H2Q2KWN37TCF5CHA3VFUK","short_pith_number":"pith:OPMT4H2Q","canonical_record":{"source":{"id":"2510.09450","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-10-10T15:00:31Z","cross_cats_sorted":[],"title_canon_sha256":"35cc60a44512c2477c98649b94802f3d4f8506012d3298970cbe1f5664677e01","abstract_canon_sha256":"f762e5affe1dab9e87f9042ac9d214191986f47537074998a6b3c202fbd623be"},"schema_version":"1.0"},"canonical_sha256":"73d93e1f50d2acddfe622f44706ea5a2af4b77faea0a4d601c5ed4a23f54b8ce","source":{"kind":"arxiv","id":"2510.09450","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.09450","created_at":"2026-05-25T02:01:08Z"},{"alias_kind":"arxiv_version","alias_value":"2510.09450v2","created_at":"2026-05-25T02:01:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.09450","created_at":"2026-05-25T02:01:08Z"},{"alias_kind":"pith_short_12","alias_value":"OPMT4H2Q2KWN","created_at":"2026-05-25T02:01:08Z"},{"alias_kind":"pith_short_16","alias_value":"OPMT4H2Q2KWN37TC","created_at":"2026-05-25T02:01:08Z"},{"alias_kind":"pith_short_8","alias_value":"OPMT4H2Q","created_at":"2026-05-25T02:01:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:OPMT4H2Q2KWN37TCF5CHA3VFUK","target":"record","payload":{"canonical_record":{"source":{"id":"2510.09450","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-10-10T15:00:31Z","cross_cats_sorted":[],"title_canon_sha256":"35cc60a44512c2477c98649b94802f3d4f8506012d3298970cbe1f5664677e01","abstract_canon_sha256":"f762e5affe1dab9e87f9042ac9d214191986f47537074998a6b3c202fbd623be"},"schema_version":"1.0"},"canonical_sha256":"73d93e1f50d2acddfe622f44706ea5a2af4b77faea0a4d601c5ed4a23f54b8ce","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:08.097334Z","signature_b64":"MC7X1wWQIEU1lPdvel9GjZ9HjdNyp4aph8BFQX0bGqaCJ3cxONWg76vT0DEo3X4eN6QUnBWKaDs5yTaOARxcAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73d93e1f50d2acddfe622f44706ea5a2af4b77faea0a4d601c5ed4a23f54b8ce","last_reissued_at":"2026-05-25T02:01:08.096538Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:08.096538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2510.09450","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-25T02:01:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yP+cSYw6uYCcfZXt0WUmLAJ9wwsYAcPC1hnBiJySYMKh2xSvvagUw+1KBTim/Bq/I12W55f61H31B7n1uQg/Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T19:05:15.346167Z"},"content_sha256":"38af6e16727e45b00032cca9f2e1d327e7d6949a7c98f22988a7e594b67a0566","schema_version":"1.0","event_id":"sha256:38af6e16727e45b00032cca9f2e1d327e7d6949a7c98f22988a7e594b67a0566"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:OPMT4H2Q2KWN37TCF5CHA3VFUK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement Under Extreme Noise","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guoxi Huang, Nantheera Anantrasirichai, Ruirui Lin","submitted_at":"2025-10-10T15:00:31Z","abstract_excerpt":"Low-light video enhancement (LLVE) is challenging due to noise, low contrast, and color degradation. While learning-based methods enable fast inference, they often fail under heavy real-world noise because they do not sufficiently exploit long-term temporal cues. We propose DWTA-Net, a novel deep-learning recurrent LLVE framework with a recurrent design. DWTA-Net adopts an integrated two-stage architecture: Stage I restores local structure and color via multi-frame alignment for temporally consistent Mamba-based enhancement, while Stage II performs recurrent refinement using a novel dynamic we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.09450","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/2510.09450/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-25T02:01:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JXaICPJ7oo4gWqyzpT27yb+QLG09KOf/QUzEQsh6aERUYmFf5/6DsTbcy0n4TWGDea5Gd6Txq36uQl/In+giDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T19:05:15.346571Z"},"content_sha256":"72c3b33869e085da4f8aa5c1a19edf5ea319360b71a0afabdaa818ba98946f6e","schema_version":"1.0","event_id":"sha256:72c3b33869e085da4f8aa5c1a19edf5ea319360b71a0afabdaa818ba98946f6e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OPMT4H2Q2KWN37TCF5CHA3VFUK/bundle.json","state_url":"https://pith.science/pith/OPMT4H2Q2KWN37TCF5CHA3VFUK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OPMT4H2Q2KWN37TCF5CHA3VFUK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-10T19:05:15Z","links":{"resolver":"https://pith.science/pith/OPMT4H2Q2KWN37TCF5CHA3VFUK","bundle":"https://pith.science/pith/OPMT4H2Q2KWN37TCF5CHA3VFUK/bundle.json","state":"https://pith.science/pith/OPMT4H2Q2KWN37TCF5CHA3VFUK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OPMT4H2Q2KWN37TCF5CHA3VFUK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:OPMT4H2Q2KWN37TCF5CHA3VFUK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f762e5affe1dab9e87f9042ac9d214191986f47537074998a6b3c202fbd623be","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-10-10T15:00:31Z","title_canon_sha256":"35cc60a44512c2477c98649b94802f3d4f8506012d3298970cbe1f5664677e01"},"schema_version":"1.0","source":{"id":"2510.09450","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.09450","created_at":"2026-05-25T02:01:08Z"},{"alias_kind":"arxiv_version","alias_value":"2510.09450v2","created_at":"2026-05-25T02:01:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.09450","created_at":"2026-05-25T02:01:08Z"},{"alias_kind":"pith_short_12","alias_value":"OPMT4H2Q2KWN","created_at":"2026-05-25T02:01:08Z"},{"alias_kind":"pith_short_16","alias_value":"OPMT4H2Q2KWN37TC","created_at":"2026-05-25T02:01:08Z"},{"alias_kind":"pith_short_8","alias_value":"OPMT4H2Q","created_at":"2026-05-25T02:01:08Z"}],"graph_snapshots":[{"event_id":"sha256:72c3b33869e085da4f8aa5c1a19edf5ea319360b71a0afabdaa818ba98946f6e","target":"graph","created_at":"2026-05-25T02:01:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2510.09450/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Low-light video enhancement (LLVE) is challenging due to noise, low contrast, and color degradation. While learning-based methods enable fast inference, they often fail under heavy real-world noise because they do not sufficiently exploit long-term temporal cues. We propose DWTA-Net, a novel deep-learning recurrent LLVE framework with a recurrent design. DWTA-Net adopts an integrated two-stage architecture: Stage I restores local structure and color via multi-frame alignment for temporally consistent Mamba-based enhancement, while Stage II performs recurrent refinement using a novel dynamic we","authors_text":"Guoxi Huang, Nantheera Anantrasirichai, Ruirui Lin","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-10-10T15:00:31Z","title":"Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement Under Extreme Noise"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.09450","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:38af6e16727e45b00032cca9f2e1d327e7d6949a7c98f22988a7e594b67a0566","target":"record","created_at":"2026-05-25T02:01:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f762e5affe1dab9e87f9042ac9d214191986f47537074998a6b3c202fbd623be","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-10-10T15:00:31Z","title_canon_sha256":"35cc60a44512c2477c98649b94802f3d4f8506012d3298970cbe1f5664677e01"},"schema_version":"1.0","source":{"id":"2510.09450","kind":"arxiv","version":2}},"canonical_sha256":"73d93e1f50d2acddfe622f44706ea5a2af4b77faea0a4d601c5ed4a23f54b8ce","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73d93e1f50d2acddfe622f44706ea5a2af4b77faea0a4d601c5ed4a23f54b8ce","first_computed_at":"2026-05-25T02:01:08.096538Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-25T02:01:08.096538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MC7X1wWQIEU1lPdvel9GjZ9HjdNyp4aph8BFQX0bGqaCJ3cxONWg76vT0DEo3X4eN6QUnBWKaDs5yTaOARxcAA==","signature_status":"signed_v1","signed_at":"2026-05-25T02:01:08.097334Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.09450","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:38af6e16727e45b00032cca9f2e1d327e7d6949a7c98f22988a7e594b67a0566","sha256:72c3b33869e085da4f8aa5c1a19edf5ea319360b71a0afabdaa818ba98946f6e"],"state_sha256":"78ffb6683c12221b13a7c30bc6de3d800a47815541e0c064d56e2bcd61d89c8d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"58TD1MkPvLPPcyshIp0rW2+HMQwfnYkU7vnCIR+B5ZbomWzUDS8Otb9ME/UAeYVe3VemiebZfCys7CakyZhvDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T19:05:15.348619Z","bundle_sha256":"99502103bb1bd8cfa08d84279e6dd210f729219572da1ed49df3b04dd80fced6"}}