{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:LW3ZNNPPQX3ZCBLVMT552PZ5FZ","short_pith_number":"pith:LW3ZNNPP","schema_version":"1.0","canonical_sha256":"5db796b5ef85f791057564fbdd3f3d2e5edc3548cf1b19606ae8f48299add6fa","source":{"kind":"arxiv","id":"1805.01934","version":1},"attestation_state":"computed","paper":{"title":"Learning to See in the Dark","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Chen Chen, Jia Xu, Qifeng Chen, Vladlen Koltun","submitted_at":"2018-05-04T21:03:12Z","abstract_excerpt":"Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can induce blur and is often impractical. A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, such as video-rate imaging at night. To support the development of learning-based pipelines for low-light image processing, we introduce a dataset of raw short-exposure low-light images, with corresponding long-exposure reference images. Using the presented dataset, we develop a pip"},"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":"1805.01934","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-04T21:03:12Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"88517d6fa59316fbad69c5a10aba44c2a96894ea5a45aa1d0a3610789f1037ec","abstract_canon_sha256":"6597bb8e13ddffb229b7a11a318459c0f322102b183e6a12b0744ade9c1738f8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:41.523511Z","signature_b64":"rIaKxB4Z7A3foH53p4vHosbtW6P/9B2To3ZE0ikHLQQz2ZV4U8GHOI8ZbYfy+xmGniRQzgRXqvysHCPylPzyBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5db796b5ef85f791057564fbdd3f3d2e5edc3548cf1b19606ae8f48299add6fa","last_reissued_at":"2026-05-18T00:16:41.522774Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:41.522774Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to See in the Dark","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Chen Chen, Jia Xu, Qifeng Chen, Vladlen Koltun","submitted_at":"2018-05-04T21:03:12Z","abstract_excerpt":"Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can induce blur and is often impractical. A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, such as video-rate imaging at night. To support the development of learning-based pipelines for low-light image processing, we introduce a dataset of raw short-exposure low-light images, with corresponding long-exposure reference images. Using the presented dataset, we develop a pip"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.01934","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":""},"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":"1805.01934","created_at":"2026-05-18T00:16:41.522903+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.01934v1","created_at":"2026-05-18T00:16:41.522903+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.01934","created_at":"2026-05-18T00:16:41.522903+00:00"},{"alias_kind":"pith_short_12","alias_value":"LW3ZNNPPQX3Z","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_16","alias_value":"LW3ZNNPPQX3ZCBLV","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_8","alias_value":"LW3ZNNPP","created_at":"2026-05-18T12:32:37.024351+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.13009","citing_title":"Deep Learning-Based Image Recovery and Pose Estimation for Resident Space Objects","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LW3ZNNPPQX3ZCBLVMT552PZ5FZ","json":"https://pith.science/pith/LW3ZNNPPQX3ZCBLVMT552PZ5FZ.json","graph_json":"https://pith.science/api/pith-number/LW3ZNNPPQX3ZCBLVMT552PZ5FZ/graph.json","events_json":"https://pith.science/api/pith-number/LW3ZNNPPQX3ZCBLVMT552PZ5FZ/events.json","paper":"https://pith.science/paper/LW3ZNNPP"},"agent_actions":{"view_html":"https://pith.science/pith/LW3ZNNPPQX3ZCBLVMT552PZ5FZ","download_json":"https://pith.science/pith/LW3ZNNPPQX3ZCBLVMT552PZ5FZ.json","view_paper":"https://pith.science/paper/LW3ZNNPP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.01934&json=true","fetch_graph":"https://pith.science/api/pith-number/LW3ZNNPPQX3ZCBLVMT552PZ5FZ/graph.json","fetch_events":"https://pith.science/api/pith-number/LW3ZNNPPQX3ZCBLVMT552PZ5FZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LW3ZNNPPQX3ZCBLVMT552PZ5FZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LW3ZNNPPQX3ZCBLVMT552PZ5FZ/action/storage_attestation","attest_author":"https://pith.science/pith/LW3ZNNPPQX3ZCBLVMT552PZ5FZ/action/author_attestation","sign_citation":"https://pith.science/pith/LW3ZNNPPQX3ZCBLVMT552PZ5FZ/action/citation_signature","submit_replication":"https://pith.science/pith/LW3ZNNPPQX3ZCBLVMT552PZ5FZ/action/replication_record"}},"created_at":"2026-05-18T00:16:41.522903+00:00","updated_at":"2026-05-18T00:16:41.522903+00:00"}