{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:AJGY2EIFYTIPF6JI7VJEHN6MEP","short_pith_number":"pith:AJGY2EIF","schema_version":"1.0","canonical_sha256":"024d8d1105c4d0f2f928fd5243b7cc23cb4980ef1afdd958a227158a04cd149d","source":{"kind":"arxiv","id":"1606.01299","version":3},"attestation_state":"computed","paper":{"title":"RAISR: Rapid and Accurate Image Super Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"John Isidoro, Peyman Milanfar, Yaniv Romano","submitted_at":"2016-06-03T22:56:49Z","abstract_excerpt":"Given an image, we wish to produce an image of larger size with significantly more pixels and higher image quality. This is generally known as the Single Image Super-Resolution (SISR) problem. The idea is that with sufficient training data (corresponding pairs of low and high resolution images) we can learn set of filters (i.e. a mapping) that when applied to given image that is not in the training set, will produce a higher resolution version of it, where the learning is preferably low complexity. In our proposed approach, the run-time is more than one to two orders of magnitude faster than t"},"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":"1606.01299","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-03T22:56:49Z","cross_cats_sorted":[],"title_canon_sha256":"6408dc0ab933a07bb2c8c3e936ea0e7ad5df8a8c87d38cf23f8f18e342ba8a97","abstract_canon_sha256":"cd03e60b4a356fb0a8b0f6f9a90fb29e7056a5b6896bb5920f2f605d30bb3167"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:03:11.760425Z","signature_b64":"UzeFQYKPpaMWkYENAHfhDtGQgKpTw771VpT0a6S2DpJE0wInpRpiOFPXja6AZ04IWM9aCt/myX9k3CCSlz0FBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"024d8d1105c4d0f2f928fd5243b7cc23cb4980ef1afdd958a227158a04cd149d","last_reissued_at":"2026-05-18T01:03:11.759739Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:03:11.759739Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RAISR: Rapid and Accurate Image Super Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"John Isidoro, Peyman Milanfar, Yaniv Romano","submitted_at":"2016-06-03T22:56:49Z","abstract_excerpt":"Given an image, we wish to produce an image of larger size with significantly more pixels and higher image quality. This is generally known as the Single Image Super-Resolution (SISR) problem. The idea is that with sufficient training data (corresponding pairs of low and high resolution images) we can learn set of filters (i.e. a mapping) that when applied to given image that is not in the training set, will produce a higher resolution version of it, where the learning is preferably low complexity. In our proposed approach, the run-time is more than one to two orders of magnitude faster than t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.01299","kind":"arxiv","version":3},"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":"1606.01299","created_at":"2026-05-18T01:03:11.759822+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.01299v3","created_at":"2026-05-18T01:03:11.759822+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.01299","created_at":"2026-05-18T01:03:11.759822+00:00"},{"alias_kind":"pith_short_12","alias_value":"AJGY2EIFYTIP","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_16","alias_value":"AJGY2EIFYTIPF6JI","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_8","alias_value":"AJGY2EIF","created_at":"2026-05-18T12:30:07.202191+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/AJGY2EIFYTIPF6JI7VJEHN6MEP","json":"https://pith.science/pith/AJGY2EIFYTIPF6JI7VJEHN6MEP.json","graph_json":"https://pith.science/api/pith-number/AJGY2EIFYTIPF6JI7VJEHN6MEP/graph.json","events_json":"https://pith.science/api/pith-number/AJGY2EIFYTIPF6JI7VJEHN6MEP/events.json","paper":"https://pith.science/paper/AJGY2EIF"},"agent_actions":{"view_html":"https://pith.science/pith/AJGY2EIFYTIPF6JI7VJEHN6MEP","download_json":"https://pith.science/pith/AJGY2EIFYTIPF6JI7VJEHN6MEP.json","view_paper":"https://pith.science/paper/AJGY2EIF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.01299&json=true","fetch_graph":"https://pith.science/api/pith-number/AJGY2EIFYTIPF6JI7VJEHN6MEP/graph.json","fetch_events":"https://pith.science/api/pith-number/AJGY2EIFYTIPF6JI7VJEHN6MEP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AJGY2EIFYTIPF6JI7VJEHN6MEP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AJGY2EIFYTIPF6JI7VJEHN6MEP/action/storage_attestation","attest_author":"https://pith.science/pith/AJGY2EIFYTIPF6JI7VJEHN6MEP/action/author_attestation","sign_citation":"https://pith.science/pith/AJGY2EIFYTIPF6JI7VJEHN6MEP/action/citation_signature","submit_replication":"https://pith.science/pith/AJGY2EIFYTIPF6JI7VJEHN6MEP/action/replication_record"}},"created_at":"2026-05-18T01:03:11.759822+00:00","updated_at":"2026-05-18T01:03:11.759822+00:00"}