{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:FMI33ZD4VATEXOXDUGAQSJIDP5","short_pith_number":"pith:FMI33ZD4","schema_version":"1.0","canonical_sha256":"2b11bde47ca8264bbae3a1810925037f79912fd3a060fdd72e67b499a2cb0209","source":{"kind":"arxiv","id":"1712.05248","version":1},"attestation_state":"computed","paper":{"title":"Image Super-resolution via Feature-augmented Random Forest","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hailiang Li, Kin-man Lam, Miaohui Wang","submitted_at":"2017-12-14T14:27:39Z","abstract_excerpt":"Recent random-forest (RF)-based image super-resolution approaches inherit some properties from dictionary-learning-based algorithms, but the effectiveness of the properties in RF is overlooked in the literature. In this paper, we present a novel feature-augmented random forest (FARF) for image super-resolution, where the conventional gradient-based features are augmented with gradient magnitudes and different feature recipes are formulated on different stages in an RF. The advantages of our method are that, firstly, the dictionary-learning-based features are enhanced by adding gradient magnitu"},"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":"1712.05248","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-14T14:27:39Z","cross_cats_sorted":[],"title_canon_sha256":"fa469ae4461b5de570fcc0673b6dd14cf657ff919cc5fbba7c9c70c158dc5036","abstract_canon_sha256":"6016a051e618b350e2dd07cc03c155f875371fdaf5290f57867428b162dca877"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:58.502398Z","signature_b64":"eeqnhsH0q2kWUj+AVDDqtWPBYEuiZ+Fg4HCKsleqR8+uQXzeYj2AbQrenpUrh3juhZwim2u9pPbu6Z0sItQgDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b11bde47ca8264bbae3a1810925037f79912fd3a060fdd72e67b499a2cb0209","last_reissued_at":"2026-05-18T00:27:58.501818Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:58.501818Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Image Super-resolution via Feature-augmented Random Forest","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hailiang Li, Kin-man Lam, Miaohui Wang","submitted_at":"2017-12-14T14:27:39Z","abstract_excerpt":"Recent random-forest (RF)-based image super-resolution approaches inherit some properties from dictionary-learning-based algorithms, but the effectiveness of the properties in RF is overlooked in the literature. In this paper, we present a novel feature-augmented random forest (FARF) for image super-resolution, where the conventional gradient-based features are augmented with gradient magnitudes and different feature recipes are formulated on different stages in an RF. The advantages of our method are that, firstly, the dictionary-learning-based features are enhanced by adding gradient magnitu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.05248","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":"1712.05248","created_at":"2026-05-18T00:27:58.501892+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.05248v1","created_at":"2026-05-18T00:27:58.501892+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.05248","created_at":"2026-05-18T00:27:58.501892+00:00"},{"alias_kind":"pith_short_12","alias_value":"FMI33ZD4VATE","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_16","alias_value":"FMI33ZD4VATEXOXD","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_8","alias_value":"FMI33ZD4","created_at":"2026-05-18T12:31:15.632608+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/FMI33ZD4VATEXOXDUGAQSJIDP5","json":"https://pith.science/pith/FMI33ZD4VATEXOXDUGAQSJIDP5.json","graph_json":"https://pith.science/api/pith-number/FMI33ZD4VATEXOXDUGAQSJIDP5/graph.json","events_json":"https://pith.science/api/pith-number/FMI33ZD4VATEXOXDUGAQSJIDP5/events.json","paper":"https://pith.science/paper/FMI33ZD4"},"agent_actions":{"view_html":"https://pith.science/pith/FMI33ZD4VATEXOXDUGAQSJIDP5","download_json":"https://pith.science/pith/FMI33ZD4VATEXOXDUGAQSJIDP5.json","view_paper":"https://pith.science/paper/FMI33ZD4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.05248&json=true","fetch_graph":"https://pith.science/api/pith-number/FMI33ZD4VATEXOXDUGAQSJIDP5/graph.json","fetch_events":"https://pith.science/api/pith-number/FMI33ZD4VATEXOXDUGAQSJIDP5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FMI33ZD4VATEXOXDUGAQSJIDP5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FMI33ZD4VATEXOXDUGAQSJIDP5/action/storage_attestation","attest_author":"https://pith.science/pith/FMI33ZD4VATEXOXDUGAQSJIDP5/action/author_attestation","sign_citation":"https://pith.science/pith/FMI33ZD4VATEXOXDUGAQSJIDP5/action/citation_signature","submit_replication":"https://pith.science/pith/FMI33ZD4VATEXOXDUGAQSJIDP5/action/replication_record"}},"created_at":"2026-05-18T00:27:58.501892+00:00","updated_at":"2026-05-18T00:27:58.501892+00:00"}