{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:SFCAZFS2HLKXPOYMQYXIABHGXA","short_pith_number":"pith:SFCAZFS2","schema_version":"1.0","canonical_sha256":"91440c965a3ad577bb0c862e8004e6b819b9ffbca7e218164d64265a4f4b73b2","source":{"kind":"arxiv","id":"1804.00256","version":1},"attestation_state":"computed","paper":{"title":"One-Two-One Networks for Compression Artifacts Reduction in Remote Sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Baochang Zhang, Chen Chen, Jianzhuang Liu, Jiaxin Gu, Jungong Han, Xianbin Cao, Xiangbo Su","submitted_at":"2018-04-01T04:44:13Z","abstract_excerpt":"Compression artifacts reduction (CAR) is a challenging problem in the field of remote sensing. Most recent deep learning based methods have demonstrated superior performance over the previous hand-crafted methods. In this paper, we propose an end-to-end one-two-one (OTO) network, to combine different deep models, i.e., summation and difference models, to solve the CAR problem. Particularly, the difference model motivated by the Laplacian pyramid is designed to obtain the high frequency information, while the summation model aggregates the low frequency information. We provide an in-depth inves"},"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":"1804.00256","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-01T04:44:13Z","cross_cats_sorted":[],"title_canon_sha256":"8ec327ab5e604576d5bf1414b912118a4455ee3fe4bc951bf9e8e6d716945f05","abstract_canon_sha256":"845547c4f57f20babede5aafc60e5e0cb907d881732618889b1d459083811ad8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:37.896415Z","signature_b64":"vd8vB4hgrmSgqNXUzGHfVPCmnBCmvBg99f5g03Dyqq7wIyxHGJ2e/p0KTUE2CD5htBnXnbw2DH9qnjzjjQ7xDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91440c965a3ad577bb0c862e8004e6b819b9ffbca7e218164d64265a4f4b73b2","last_reissued_at":"2026-05-18T00:19:37.895651Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:37.895651Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"One-Two-One Networks for Compression Artifacts Reduction in Remote Sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Baochang Zhang, Chen Chen, Jianzhuang Liu, Jiaxin Gu, Jungong Han, Xianbin Cao, Xiangbo Su","submitted_at":"2018-04-01T04:44:13Z","abstract_excerpt":"Compression artifacts reduction (CAR) is a challenging problem in the field of remote sensing. Most recent deep learning based methods have demonstrated superior performance over the previous hand-crafted methods. In this paper, we propose an end-to-end one-two-one (OTO) network, to combine different deep models, i.e., summation and difference models, to solve the CAR problem. Particularly, the difference model motivated by the Laplacian pyramid is designed to obtain the high frequency information, while the summation model aggregates the low frequency information. We provide an in-depth inves"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.00256","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":"1804.00256","created_at":"2026-05-18T00:19:37.895775+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.00256v1","created_at":"2026-05-18T00:19:37.895775+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.00256","created_at":"2026-05-18T00:19:37.895775+00:00"},{"alias_kind":"pith_short_12","alias_value":"SFCAZFS2HLKX","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"SFCAZFS2HLKXPOYM","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"SFCAZFS2","created_at":"2026-05-18T12:32:53.628368+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/SFCAZFS2HLKXPOYMQYXIABHGXA","json":"https://pith.science/pith/SFCAZFS2HLKXPOYMQYXIABHGXA.json","graph_json":"https://pith.science/api/pith-number/SFCAZFS2HLKXPOYMQYXIABHGXA/graph.json","events_json":"https://pith.science/api/pith-number/SFCAZFS2HLKXPOYMQYXIABHGXA/events.json","paper":"https://pith.science/paper/SFCAZFS2"},"agent_actions":{"view_html":"https://pith.science/pith/SFCAZFS2HLKXPOYMQYXIABHGXA","download_json":"https://pith.science/pith/SFCAZFS2HLKXPOYMQYXIABHGXA.json","view_paper":"https://pith.science/paper/SFCAZFS2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.00256&json=true","fetch_graph":"https://pith.science/api/pith-number/SFCAZFS2HLKXPOYMQYXIABHGXA/graph.json","fetch_events":"https://pith.science/api/pith-number/SFCAZFS2HLKXPOYMQYXIABHGXA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SFCAZFS2HLKXPOYMQYXIABHGXA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SFCAZFS2HLKXPOYMQYXIABHGXA/action/storage_attestation","attest_author":"https://pith.science/pith/SFCAZFS2HLKXPOYMQYXIABHGXA/action/author_attestation","sign_citation":"https://pith.science/pith/SFCAZFS2HLKXPOYMQYXIABHGXA/action/citation_signature","submit_replication":"https://pith.science/pith/SFCAZFS2HLKXPOYMQYXIABHGXA/action/replication_record"}},"created_at":"2026-05-18T00:19:37.895775+00:00","updated_at":"2026-05-18T00:19:37.895775+00:00"}