{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:GDW6IKKCNQMQKOBKCB673HGJ23","short_pith_number":"pith:GDW6IKKC","canonical_record":{"source":{"id":"1906.12021","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-28T02:32:44Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"e07eb624e17e74d9175b8c123bbddafff44f0522452614699e6a9d3c9dee8ea2","abstract_canon_sha256":"64966647c8bfd8b87f4dee895d2c986628bd37d4c63d719fb3ba6e4a630af433"},"schema_version":"1.0"},"canonical_sha256":"30ede429426c1905382a107dfd9cc9d6d3ab8b5015de4dcde45d295c4244f5c6","source":{"kind":"arxiv","id":"1906.12021","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.12021","created_at":"2026-05-17T23:41:52Z"},{"alias_kind":"arxiv_version","alias_value":"1906.12021v2","created_at":"2026-05-17T23:41:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.12021","created_at":"2026-05-17T23:41:52Z"},{"alias_kind":"pith_short_12","alias_value":"GDW6IKKCNQMQ","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"GDW6IKKCNQMQKOBK","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"GDW6IKKC","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:GDW6IKKCNQMQKOBKCB673HGJ23","target":"record","payload":{"canonical_record":{"source":{"id":"1906.12021","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-28T02:32:44Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"e07eb624e17e74d9175b8c123bbddafff44f0522452614699e6a9d3c9dee8ea2","abstract_canon_sha256":"64966647c8bfd8b87f4dee895d2c986628bd37d4c63d719fb3ba6e4a630af433"},"schema_version":"1.0"},"canonical_sha256":"30ede429426c1905382a107dfd9cc9d6d3ab8b5015de4dcde45d295c4244f5c6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:52.638091Z","signature_b64":"FvzItXdbzPjqLiSVY8YDJ1wle52nyArA3chdWDR3MXdhbi9Zl6hiKgVwo8fQEuD9WQyTixiERMfWDKE1iDALDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"30ede429426c1905382a107dfd9cc9d6d3ab8b5015de4dcde45d295c4244f5c6","last_reissued_at":"2026-05-17T23:41:52.637569Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:52.637569Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.12021","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:41:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g6pU15uXHWyYm731uaEBrZE18p17VTKyFgvOm+DIuiHW9PrjGjtvwqDOAciE2R3Lx5ZzpZk2y+KfrYvbKC+FCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T18:27:40.457107Z"},"content_sha256":"6bf8495f9e32e895ae8b5594874f4d5c192442d9752e193a35899770115d9cce","schema_version":"1.0","event_id":"sha256:6bf8495f9e32e895ae8b5594874f4d5c192442d9752e193a35899770115d9cce"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:GDW6IKKCNQMQKOBKCB673HGJ23","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Densely Residual Laplacian Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Nick Barnes, Saeed Anwar","submitted_at":"2019-06-28T02:32:44Z","abstract_excerpt":"Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architectures and long training times. Furthermore, current convolutional neural networks for super-resolution are unable to exploit features at multiple scales and weigh them equally, limiting their learning capability. In this exposition, we present a compact and accurate super-resolution algorithm namely, Densely Residual Laplacian Network (DRLN). The proposed network employs cascading residual on the residual structure to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.12021","kind":"arxiv","version":2},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:41:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mt04yviyd2UU9emIIddEZwDO4gFnXTAGSwcTBCbLqt8u6Q6RgiB/qq+SYf7FsRZLF6EUUZMyi4O1AQcE7LLbBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T18:27:40.457675Z"},"content_sha256":"e94478869f5c820130a5f250d8695ac2eaf60ee23b5b1d17245f02a49ca1aa5f","schema_version":"1.0","event_id":"sha256:e94478869f5c820130a5f250d8695ac2eaf60ee23b5b1d17245f02a49ca1aa5f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GDW6IKKCNQMQKOBKCB673HGJ23/bundle.json","state_url":"https://pith.science/pith/GDW6IKKCNQMQKOBKCB673HGJ23/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GDW6IKKCNQMQKOBKCB673HGJ23/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-03T18:27:40Z","links":{"resolver":"https://pith.science/pith/GDW6IKKCNQMQKOBKCB673HGJ23","bundle":"https://pith.science/pith/GDW6IKKCNQMQKOBKCB673HGJ23/bundle.json","state":"https://pith.science/pith/GDW6IKKCNQMQKOBKCB673HGJ23/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GDW6IKKCNQMQKOBKCB673HGJ23/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:GDW6IKKCNQMQKOBKCB673HGJ23","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"64966647c8bfd8b87f4dee895d2c986628bd37d4c63d719fb3ba6e4a630af433","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-28T02:32:44Z","title_canon_sha256":"e07eb624e17e74d9175b8c123bbddafff44f0522452614699e6a9d3c9dee8ea2"},"schema_version":"1.0","source":{"id":"1906.12021","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.12021","created_at":"2026-05-17T23:41:52Z"},{"alias_kind":"arxiv_version","alias_value":"1906.12021v2","created_at":"2026-05-17T23:41:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.12021","created_at":"2026-05-17T23:41:52Z"},{"alias_kind":"pith_short_12","alias_value":"GDW6IKKCNQMQ","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"GDW6IKKCNQMQKOBK","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"GDW6IKKC","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:e94478869f5c820130a5f250d8695ac2eaf60ee23b5b1d17245f02a49ca1aa5f","target":"graph","created_at":"2026-05-17T23:41:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architectures and long training times. Furthermore, current convolutional neural networks for super-resolution are unable to exploit features at multiple scales and weigh them equally, limiting their learning capability. In this exposition, we present a compact and accurate super-resolution algorithm namely, Densely Residual Laplacian Network (DRLN). The proposed network employs cascading residual on the residual structure to","authors_text":"Nick Barnes, Saeed Anwar","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-28T02:32:44Z","title":"Densely Residual Laplacian Super-Resolution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.12021","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6bf8495f9e32e895ae8b5594874f4d5c192442d9752e193a35899770115d9cce","target":"record","created_at":"2026-05-17T23:41:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"64966647c8bfd8b87f4dee895d2c986628bd37d4c63d719fb3ba6e4a630af433","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-28T02:32:44Z","title_canon_sha256":"e07eb624e17e74d9175b8c123bbddafff44f0522452614699e6a9d3c9dee8ea2"},"schema_version":"1.0","source":{"id":"1906.12021","kind":"arxiv","version":2}},"canonical_sha256":"30ede429426c1905382a107dfd9cc9d6d3ab8b5015de4dcde45d295c4244f5c6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"30ede429426c1905382a107dfd9cc9d6d3ab8b5015de4dcde45d295c4244f5c6","first_computed_at":"2026-05-17T23:41:52.637569Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:52.637569Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FvzItXdbzPjqLiSVY8YDJ1wle52nyArA3chdWDR3MXdhbi9Zl6hiKgVwo8fQEuD9WQyTixiERMfWDKE1iDALDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:52.638091Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.12021","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6bf8495f9e32e895ae8b5594874f4d5c192442d9752e193a35899770115d9cce","sha256:e94478869f5c820130a5f250d8695ac2eaf60ee23b5b1d17245f02a49ca1aa5f"],"state_sha256":"a9ff02092f37eff27a3247e412a5dd3e885e592fdd7e1e95c804faf18d5da178"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UW9zDw7d3LHMzrMf91ftW7PWVjke504maTEfNz1+D8KgpHGQ2D+je5wKQMuAA3MsxnK0XrQaD/m4VU4tf1oZAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T18:27:40.460385Z","bundle_sha256":"1847d30bff1118e57ebeb3004ca02c4c53faf50d6a9296c9e8b2261a8e625883"}}