{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:D5WW73IUFPJIAARJLEN5OZ6BAW","short_pith_number":"pith:D5WW73IU","schema_version":"1.0","canonical_sha256":"1f6d6fed142bd2800229591bd767c1058778808ab389b79f9dfebfd5e468be96","source":{"kind":"arxiv","id":"1808.01491","version":1},"attestation_state":"computed","paper":{"title":"Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanbin Li, Huiyou Chang, Le Dong, Liang Lin, Wei Zhang, Xiang He","submitted_at":"2018-08-04T15:09:01Z","abstract_excerpt":"Single image rain streaks removal has recently witnessed substantial progress due to the development of deep convolutional neural networks. However, existing deep learning based methods either focus on the entrance and exit of the network by decomposing the input image into high and low frequency information and employing residual learning to reduce the mapping range, or focus on the introduction of cascaded learning scheme to decompose the task of rain streaks removal into multi-stages. These methods treat the convolutional neural network as an encapsulated end-to-end mapping module without d"},"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":"1808.01491","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-04T15:09:01Z","cross_cats_sorted":[],"title_canon_sha256":"494c8e13a6736e58bd73a19c1a6007d0aca146e64cc4357fb949dd1c5171df00","abstract_canon_sha256":"df40b8ad275e219354d3064a6ff2a129721d664c7212d662f22f2dae5d8e3b1d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:52.781831Z","signature_b64":"WYAT412aapZYm3ao4KBYhAT1RBAr8FG4mjmy8f5pOjlzSn3v8XoQQTfRYr9JdPejTRGM0fIsJJGCaOj2XWq+CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f6d6fed142bd2800229591bd767c1058778808ab389b79f9dfebfd5e468be96","last_reissued_at":"2026-05-18T00:08:52.781073Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:52.781073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanbin Li, Huiyou Chang, Le Dong, Liang Lin, Wei Zhang, Xiang He","submitted_at":"2018-08-04T15:09:01Z","abstract_excerpt":"Single image rain streaks removal has recently witnessed substantial progress due to the development of deep convolutional neural networks. However, existing deep learning based methods either focus on the entrance and exit of the network by decomposing the input image into high and low frequency information and employing residual learning to reduce the mapping range, or focus on the introduction of cascaded learning scheme to decompose the task of rain streaks removal into multi-stages. These methods treat the convolutional neural network as an encapsulated end-to-end mapping module without d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.01491","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":"1808.01491","created_at":"2026-05-18T00:08:52.781193+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.01491v1","created_at":"2026-05-18T00:08:52.781193+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.01491","created_at":"2026-05-18T00:08:52.781193+00:00"},{"alias_kind":"pith_short_12","alias_value":"D5WW73IUFPJI","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"D5WW73IUFPJIAARJ","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"D5WW73IU","created_at":"2026-05-18T12:32:19.392346+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/D5WW73IUFPJIAARJLEN5OZ6BAW","json":"https://pith.science/pith/D5WW73IUFPJIAARJLEN5OZ6BAW.json","graph_json":"https://pith.science/api/pith-number/D5WW73IUFPJIAARJLEN5OZ6BAW/graph.json","events_json":"https://pith.science/api/pith-number/D5WW73IUFPJIAARJLEN5OZ6BAW/events.json","paper":"https://pith.science/paper/D5WW73IU"},"agent_actions":{"view_html":"https://pith.science/pith/D5WW73IUFPJIAARJLEN5OZ6BAW","download_json":"https://pith.science/pith/D5WW73IUFPJIAARJLEN5OZ6BAW.json","view_paper":"https://pith.science/paper/D5WW73IU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.01491&json=true","fetch_graph":"https://pith.science/api/pith-number/D5WW73IUFPJIAARJLEN5OZ6BAW/graph.json","fetch_events":"https://pith.science/api/pith-number/D5WW73IUFPJIAARJLEN5OZ6BAW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D5WW73IUFPJIAARJLEN5OZ6BAW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D5WW73IUFPJIAARJLEN5OZ6BAW/action/storage_attestation","attest_author":"https://pith.science/pith/D5WW73IUFPJIAARJLEN5OZ6BAW/action/author_attestation","sign_citation":"https://pith.science/pith/D5WW73IUFPJIAARJLEN5OZ6BAW/action/citation_signature","submit_replication":"https://pith.science/pith/D5WW73IUFPJIAARJLEN5OZ6BAW/action/replication_record"}},"created_at":"2026-05-18T00:08:52.781193+00:00","updated_at":"2026-05-18T00:08:52.781193+00:00"}