{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:DQPGFU6GTARV2H7PONOVJEGA6Y","short_pith_number":"pith:DQPGFU6G","canonical_record":{"source":{"id":"1710.08124","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-23T07:39:35Z","cross_cats_sorted":[],"title_canon_sha256":"8a854728719886c081a28cbd2545392a2d805c374bf060f6932f44c3cad07af1","abstract_canon_sha256":"0f94f86ccd8d9c8f87a3e0c8b7ccc5b84dd477b41e0c6162795025f394bd9777"},"schema_version":"1.0"},"canonical_sha256":"1c1e62d3c698235d1fef735d5490c0f6105b3a4516724c8d84a222ceb035b226","source":{"kind":"arxiv","id":"1710.08124","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.08124","created_at":"2026-05-18T00:32:17Z"},{"alias_kind":"arxiv_version","alias_value":"1710.08124v1","created_at":"2026-05-18T00:32:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.08124","created_at":"2026-05-18T00:32:17Z"},{"alias_kind":"pith_short_12","alias_value":"DQPGFU6GTARV","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"DQPGFU6GTARV2H7P","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"DQPGFU6G","created_at":"2026-05-18T12:31:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:DQPGFU6GTARV2H7PONOVJEGA6Y","target":"record","payload":{"canonical_record":{"source":{"id":"1710.08124","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-23T07:39:35Z","cross_cats_sorted":[],"title_canon_sha256":"8a854728719886c081a28cbd2545392a2d805c374bf060f6932f44c3cad07af1","abstract_canon_sha256":"0f94f86ccd8d9c8f87a3e0c8b7ccc5b84dd477b41e0c6162795025f394bd9777"},"schema_version":"1.0"},"canonical_sha256":"1c1e62d3c698235d1fef735d5490c0f6105b3a4516724c8d84a222ceb035b226","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:17.125122Z","signature_b64":"5RJfMfxySKw7T5Wl0RyZymOzCv0pTgTOnRVZfKQvbaOgEVbBDd1LFd6skjpbwH3/6DJo8wxJNFonJS9k/AgNCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c1e62d3c698235d1fef735d5490c0f6105b3a4516724c8d84a222ceb035b226","last_reissued_at":"2026-05-18T00:32:17.124578Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:17.124578Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.08124","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:32:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"77V3UgNdrqXcTpHWvp9OnetU3RSfEjCMmi56d3ZdFkR4hvDucbRN1aWLSW0gebhBQwFOD84GUg93T55AxdsPDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T18:18:20.311889Z"},"content_sha256":"073ed1f1ef4e0ec586292b7aee24cf6da58a2c8a943a6d88ffe5a454ec45ebb0","schema_version":"1.0","event_id":"sha256:073ed1f1ef4e0ec586292b7aee24cf6da58a2c8a943a6d88ffe5a454ec45ebb0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:DQPGFU6GTARV2H7PONOVJEGA6Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100$\\times$ speed-up","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Charles-Alban Deledalle (IMB, IOGS), Lo\\\"ic Denis (UJM, Shibin Parameswaran (UC San Diego), Truong Q. Nguyen (UC San Diego), UC San Diego)","submitted_at":"2017-10-23T07:39:35Z","abstract_excerpt":"Image restoration methods aim to recover the underlying clean image from corrupted observations. The Expected Patch Log-likelihood (EPLL) algorithm is a powerful image restoration method that uses a Gaussian mixture model (GMM) prior on the patches of natural images. Although it is very effective for restoring images, its high runtime complexity makes EPLL ill-suited for most practical applications. In this paper, we propose three approximations to the original EPLL algorithm. The resulting algorithm, which we call the fast-EPLL (FEPLL), attains a dramatic speed-up of two orders of magnitude o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.08124","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:32:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"utX8MoktTeaDtKRq7RZg8Ye9G8vDcTFDZ8xPpv8TGAneBslzR26+cSuv4jLaBy5mfPQcJmSZ9XXuHQH0GXEqAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T18:18:20.312551Z"},"content_sha256":"cdc76b8d8b9aa3eb3cdc355306d98b36952ceee448bce3cd8428bfbfd30434c5","schema_version":"1.0","event_id":"sha256:cdc76b8d8b9aa3eb3cdc355306d98b36952ceee448bce3cd8428bfbfd30434c5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DQPGFU6GTARV2H7PONOVJEGA6Y/bundle.json","state_url":"https://pith.science/pith/DQPGFU6GTARV2H7PONOVJEGA6Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DQPGFU6GTARV2H7PONOVJEGA6Y/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":"2026-05-26T18:18:20Z","links":{"resolver":"https://pith.science/pith/DQPGFU6GTARV2H7PONOVJEGA6Y","bundle":"https://pith.science/pith/DQPGFU6GTARV2H7PONOVJEGA6Y/bundle.json","state":"https://pith.science/pith/DQPGFU6GTARV2H7PONOVJEGA6Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DQPGFU6GTARV2H7PONOVJEGA6Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:DQPGFU6GTARV2H7PONOVJEGA6Y","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":"0f94f86ccd8d9c8f87a3e0c8b7ccc5b84dd477b41e0c6162795025f394bd9777","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-23T07:39:35Z","title_canon_sha256":"8a854728719886c081a28cbd2545392a2d805c374bf060f6932f44c3cad07af1"},"schema_version":"1.0","source":{"id":"1710.08124","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.08124","created_at":"2026-05-18T00:32:17Z"},{"alias_kind":"arxiv_version","alias_value":"1710.08124v1","created_at":"2026-05-18T00:32:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.08124","created_at":"2026-05-18T00:32:17Z"},{"alias_kind":"pith_short_12","alias_value":"DQPGFU6GTARV","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"DQPGFU6GTARV2H7P","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"DQPGFU6G","created_at":"2026-05-18T12:31:12Z"}],"graph_snapshots":[{"event_id":"sha256:cdc76b8d8b9aa3eb3cdc355306d98b36952ceee448bce3cd8428bfbfd30434c5","target":"graph","created_at":"2026-05-18T00:32:17Z","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":"Image restoration methods aim to recover the underlying clean image from corrupted observations. The Expected Patch Log-likelihood (EPLL) algorithm is a powerful image restoration method that uses a Gaussian mixture model (GMM) prior on the patches of natural images. Although it is very effective for restoring images, its high runtime complexity makes EPLL ill-suited for most practical applications. In this paper, we propose three approximations to the original EPLL algorithm. The resulting algorithm, which we call the fast-EPLL (FEPLL), attains a dramatic speed-up of two orders of magnitude o","authors_text":"Charles-Alban Deledalle (IMB, IOGS), Lo\\\"ic Denis (UJM, Shibin Parameswaran (UC San Diego), Truong Q. Nguyen (UC San Diego), UC San Diego)","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-23T07:39:35Z","title":"Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100$\\times$ speed-up"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.08124","kind":"arxiv","version":1},"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":"sha256:073ed1f1ef4e0ec586292b7aee24cf6da58a2c8a943a6d88ffe5a454ec45ebb0","target":"record","created_at":"2026-05-18T00:32:17Z","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":"0f94f86ccd8d9c8f87a3e0c8b7ccc5b84dd477b41e0c6162795025f394bd9777","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-23T07:39:35Z","title_canon_sha256":"8a854728719886c081a28cbd2545392a2d805c374bf060f6932f44c3cad07af1"},"schema_version":"1.0","source":{"id":"1710.08124","kind":"arxiv","version":1}},"canonical_sha256":"1c1e62d3c698235d1fef735d5490c0f6105b3a4516724c8d84a222ceb035b226","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1c1e62d3c698235d1fef735d5490c0f6105b3a4516724c8d84a222ceb035b226","first_computed_at":"2026-05-18T00:32:17.124578Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:32:17.124578Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5RJfMfxySKw7T5Wl0RyZymOzCv0pTgTOnRVZfKQvbaOgEVbBDd1LFd6skjpbwH3/6DJo8wxJNFonJS9k/AgNCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:32:17.125122Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.08124","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:073ed1f1ef4e0ec586292b7aee24cf6da58a2c8a943a6d88ffe5a454ec45ebb0","sha256:cdc76b8d8b9aa3eb3cdc355306d98b36952ceee448bce3cd8428bfbfd30434c5"],"state_sha256":"b1f7eb5e4b016b0119f6913cfde1598a2c14d00e5ed2d16df791d91f2b5320f2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rXen1lC/nc1kUzigrtNV4rfwXaoPtzGQvnF27oqaCS+rhJo06SHH/KJ+CfDfAsJA4G8DVYsoqOIDop3XYl1hCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T18:18:20.315802Z","bundle_sha256":"992c84338a208b4e328ce40bfcd1dc91388829a410113984b23e9d99d5c83411"}}