{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:DBVUVWB4LMRGUMXQKH33XASL3S","short_pith_number":"pith:DBVUVWB4","canonical_record":{"source":{"id":"1906.11129","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-12T17:13:50Z","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"title_canon_sha256":"f754cd7b7f3659f5045577b43b801c101d59afa7b4acb7735b311c89155e96f9","abstract_canon_sha256":"dc2ff08be030b0c3c6f7ca3cf88b927ec76bead0c0ebe466adceaa883cead6de"},"schema_version":"1.0"},"canonical_sha256":"186b4ad83c5b226a32f051f7bb824bdc9ceffc4a98aa4aa7311e570d3d2a3c69","source":{"kind":"arxiv","id":"1906.11129","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.11129","created_at":"2026-05-17T23:42:09Z"},{"alias_kind":"arxiv_version","alias_value":"1906.11129v1","created_at":"2026-05-17T23:42:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11129","created_at":"2026-05-17T23:42:09Z"},{"alias_kind":"pith_short_12","alias_value":"DBVUVWB4LMRG","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"DBVUVWB4LMRGUMXQ","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"DBVUVWB4","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:DBVUVWB4LMRGUMXQKH33XASL3S","target":"record","payload":{"canonical_record":{"source":{"id":"1906.11129","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-12T17:13:50Z","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"title_canon_sha256":"f754cd7b7f3659f5045577b43b801c101d59afa7b4acb7735b311c89155e96f9","abstract_canon_sha256":"dc2ff08be030b0c3c6f7ca3cf88b927ec76bead0c0ebe466adceaa883cead6de"},"schema_version":"1.0"},"canonical_sha256":"186b4ad83c5b226a32f051f7bb824bdc9ceffc4a98aa4aa7311e570d3d2a3c69","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:09.741639Z","signature_b64":"WijezRMzHvpZAilaHiA5cCPVZ5tEqMAyXrjsNN/Tx41XReHeOPhnehU7nz87MC6k1aSmyAFL1c1jhLFgf8xdBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"186b4ad83c5b226a32f051f7bb824bdc9ceffc4a98aa4aa7311e570d3d2a3c69","last_reissued_at":"2026-05-17T23:42:09.740982Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:09.740982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.11129","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":"2026-05-17T23:42:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hmEBC83rmQlQF3G8DAvj0yT2XPCQvQHUpFFuLyLbpHXjDd92lhyKCAE5Shmebl+ThP/TWluH2Bf1fOoeSoUjDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T03:12:42.861273Z"},"content_sha256":"8babfa93a7c0c77b716f63e513f484273e489cfdfc558daa3f7b07123cb7e8de","schema_version":"1.0","event_id":"sha256:8babfa93a7c0c77b716f63e513f484273e489cfdfc558daa3f7b07123cb7e8de"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:DBVUVWB4LMRGUMXQKH33XASL3S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV","stat.ML"],"primary_cat":"cs.CV","authors_text":"Rajeev Yasarla, Vishal M. Patel","submitted_at":"2019-06-12T17:13:50Z","abstract_excerpt":"Single image de-raining is an extremely challenging problem since the rainy image may contain rain streaks which may vary in size, direction and density. Previous approaches have attempted to address this problem by leveraging some prior information to remove rain streaks from a single image. One of the major limitations of these approaches is that they do not consider the location information of rain drops in the image. The proposed Uncertainty guided Multi-scale Residual Learning (UMRL) network attempts to address this issue by learning the rain content at different scales and using them to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11129","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":"2026-05-17T23:42:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UHW7rOFoPecXQzA1tPSDNyEQ2unuJqYVPpxOxHUesrb3nOEkCwY2KbEXY5RJnBDuF0Uw0F1gD87HKEDJlplZDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T03:12:42.861630Z"},"content_sha256":"9803aaf0aa8dee764a20ec5e7622c73c6009d69372204de54ff68e0c0902f7d6","schema_version":"1.0","event_id":"sha256:9803aaf0aa8dee764a20ec5e7622c73c6009d69372204de54ff68e0c0902f7d6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DBVUVWB4LMRGUMXQKH33XASL3S/bundle.json","state_url":"https://pith.science/pith/DBVUVWB4LMRGUMXQKH33XASL3S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DBVUVWB4LMRGUMXQKH33XASL3S/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":"2026-06-03T03:12:42Z","links":{"resolver":"https://pith.science/pith/DBVUVWB4LMRGUMXQKH33XASL3S","bundle":"https://pith.science/pith/DBVUVWB4LMRGUMXQKH33XASL3S/bundle.json","state":"https://pith.science/pith/DBVUVWB4LMRGUMXQKH33XASL3S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DBVUVWB4LMRGUMXQKH33XASL3S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:DBVUVWB4LMRGUMXQKH33XASL3S","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":"dc2ff08be030b0c3c6f7ca3cf88b927ec76bead0c0ebe466adceaa883cead6de","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-12T17:13:50Z","title_canon_sha256":"f754cd7b7f3659f5045577b43b801c101d59afa7b4acb7735b311c89155e96f9"},"schema_version":"1.0","source":{"id":"1906.11129","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.11129","created_at":"2026-05-17T23:42:09Z"},{"alias_kind":"arxiv_version","alias_value":"1906.11129v1","created_at":"2026-05-17T23:42:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11129","created_at":"2026-05-17T23:42:09Z"},{"alias_kind":"pith_short_12","alias_value":"DBVUVWB4LMRG","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"DBVUVWB4LMRGUMXQ","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"DBVUVWB4","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:9803aaf0aa8dee764a20ec5e7622c73c6009d69372204de54ff68e0c0902f7d6","target":"graph","created_at":"2026-05-17T23:42:09Z","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":"Single image de-raining is an extremely challenging problem since the rainy image may contain rain streaks which may vary in size, direction and density. Previous approaches have attempted to address this problem by leveraging some prior information to remove rain streaks from a single image. One of the major limitations of these approaches is that they do not consider the location information of rain drops in the image. The proposed Uncertainty guided Multi-scale Residual Learning (UMRL) network attempts to address this issue by learning the rain content at different scales and using them to ","authors_text":"Rajeev Yasarla, Vishal M. Patel","cross_cats":["cs.LG","eess.IV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-12T17:13:50Z","title":"Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11129","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":"sha256:8babfa93a7c0c77b716f63e513f484273e489cfdfc558daa3f7b07123cb7e8de","target":"record","created_at":"2026-05-17T23:42:09Z","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":"dc2ff08be030b0c3c6f7ca3cf88b927ec76bead0c0ebe466adceaa883cead6de","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-12T17:13:50Z","title_canon_sha256":"f754cd7b7f3659f5045577b43b801c101d59afa7b4acb7735b311c89155e96f9"},"schema_version":"1.0","source":{"id":"1906.11129","kind":"arxiv","version":1}},"canonical_sha256":"186b4ad83c5b226a32f051f7bb824bdc9ceffc4a98aa4aa7311e570d3d2a3c69","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"186b4ad83c5b226a32f051f7bb824bdc9ceffc4a98aa4aa7311e570d3d2a3c69","first_computed_at":"2026-05-17T23:42:09.740982Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:42:09.740982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WijezRMzHvpZAilaHiA5cCPVZ5tEqMAyXrjsNN/Tx41XReHeOPhnehU7nz87MC6k1aSmyAFL1c1jhLFgf8xdBw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:42:09.741639Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.11129","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8babfa93a7c0c77b716f63e513f484273e489cfdfc558daa3f7b07123cb7e8de","sha256:9803aaf0aa8dee764a20ec5e7622c73c6009d69372204de54ff68e0c0902f7d6"],"state_sha256":"5486fcf3710db26f4e1dc0c5ba70799cb269bd7db7c6315ccd06127f98a4440f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aNIjzvDcnrqR3ATcvYsXxJUwx3DRkuy08o0zA9oWVfXIpPcGKoiw3ngJU8yT1QeZrM72Wf2ceKpODF8N8McwBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T03:12:42.863929Z","bundle_sha256":"80943b29909cce6ac2acc015ddfaee9bd70b6a46d14c157c5a58890f9c5ccaec"}}