{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:N4AVGAD4V7RJEBCQLAPQGSXAET","short_pith_number":"pith:N4AVGAD4","canonical_record":{"source":{"id":"1704.00275","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-02T09:44:05Z","cross_cats_sorted":[],"title_canon_sha256":"0d1b78e8e9adec20f1adfbe2ce855946a67a3766b35c3a01afbe0d56e231151e","abstract_canon_sha256":"560a34a2ad56f3f3e5ccfb441202e8fe2f640cffd4f50fa1c71686cf52eb11ff"},"schema_version":"1.0"},"canonical_sha256":"6f0153007cafe2920450581f034ae024c84c93fdaf9cbc35d58b797ae37d3d68","source":{"kind":"arxiv","id":"1704.00275","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.00275","created_at":"2026-05-18T00:45:06Z"},{"alias_kind":"arxiv_version","alias_value":"1704.00275v2","created_at":"2026-05-18T00:45:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.00275","created_at":"2026-05-18T00:45:06Z"},{"alias_kind":"pith_short_12","alias_value":"N4AVGAD4V7RJ","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"N4AVGAD4V7RJEBCQ","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"N4AVGAD4","created_at":"2026-05-18T12:31:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:N4AVGAD4V7RJEBCQLAPQGSXAET","target":"record","payload":{"canonical_record":{"source":{"id":"1704.00275","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-02T09:44:05Z","cross_cats_sorted":[],"title_canon_sha256":"0d1b78e8e9adec20f1adfbe2ce855946a67a3766b35c3a01afbe0d56e231151e","abstract_canon_sha256":"560a34a2ad56f3f3e5ccfb441202e8fe2f640cffd4f50fa1c71686cf52eb11ff"},"schema_version":"1.0"},"canonical_sha256":"6f0153007cafe2920450581f034ae024c84c93fdaf9cbc35d58b797ae37d3d68","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:06.046731Z","signature_b64":"thUq2KZ+mECy79AIXNFdPAjtJIrxEemchYLYyr1Sqttb5fySWitTfZhBWU+LkcDlp3KIJWe5FLuGoBNowxGdDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f0153007cafe2920450581f034ae024c84c93fdaf9cbc35d58b797ae37d3d68","last_reissued_at":"2026-05-18T00:45:06.046371Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:06.046371Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1704.00275","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":"2026-05-18T00:45:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rACe7K1HH0fIU0QCvnT+hOUVgKFp3otVafXdFC8Dre96H+Vv23rhw7YOzEHI/GdpNAAvD8kIvzosBWawRXkBAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T13:44:46.689133Z"},"content_sha256":"0f53289927b91a45094cb0e88218313be29652185a3012215e7690e9f6f518f9","schema_version":"1.0","event_id":"sha256:0f53289927b91a45094cb0e88218313be29652185a3012215e7690e9f6f518f9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:N4AVGAD4V7RJEBCQLAPQGSXAET","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SAR image despeckling through convolutional neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"D. Cozzolino, G. Chierchia, G. Poggi, L. Verdoliva","submitted_at":"2017-04-02T09:44:05Z","abstract_excerpt":"In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitemporal SAR image and its multilook version, in order to approximate a clean image. Experimental results, both on synthetic and real SAR data, show the method to achieve better performance with respect to state-of-the-art techniques."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.00275","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":"2026-05-18T00:45:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bJ51sxOoxT84e93EQFa2GLUB+Q3J6LMh9vGKUH7BeBnHMFOjMcOCkk0MtdZgJp4NILHWGcfK7D51HOFjw4JvAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T13:44:46.689497Z"},"content_sha256":"06aa99765852d95b85224fc9cfe14d02a4c3c2cc8bc3ada044882bef8adfde2a","schema_version":"1.0","event_id":"sha256:06aa99765852d95b85224fc9cfe14d02a4c3c2cc8bc3ada044882bef8adfde2a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N4AVGAD4V7RJEBCQLAPQGSXAET/bundle.json","state_url":"https://pith.science/pith/N4AVGAD4V7RJEBCQLAPQGSXAET/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N4AVGAD4V7RJEBCQLAPQGSXAET/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":"2026-06-28T13:44:46Z","links":{"resolver":"https://pith.science/pith/N4AVGAD4V7RJEBCQLAPQGSXAET","bundle":"https://pith.science/pith/N4AVGAD4V7RJEBCQLAPQGSXAET/bundle.json","state":"https://pith.science/pith/N4AVGAD4V7RJEBCQLAPQGSXAET/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N4AVGAD4V7RJEBCQLAPQGSXAET/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:N4AVGAD4V7RJEBCQLAPQGSXAET","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":"560a34a2ad56f3f3e5ccfb441202e8fe2f640cffd4f50fa1c71686cf52eb11ff","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-02T09:44:05Z","title_canon_sha256":"0d1b78e8e9adec20f1adfbe2ce855946a67a3766b35c3a01afbe0d56e231151e"},"schema_version":"1.0","source":{"id":"1704.00275","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.00275","created_at":"2026-05-18T00:45:06Z"},{"alias_kind":"arxiv_version","alias_value":"1704.00275v2","created_at":"2026-05-18T00:45:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.00275","created_at":"2026-05-18T00:45:06Z"},{"alias_kind":"pith_short_12","alias_value":"N4AVGAD4V7RJ","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"N4AVGAD4V7RJEBCQ","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"N4AVGAD4","created_at":"2026-05-18T12:31:31Z"}],"graph_snapshots":[{"event_id":"sha256:06aa99765852d95b85224fc9cfe14d02a4c3c2cc8bc3ada044882bef8adfde2a","target":"graph","created_at":"2026-05-18T00:45:06Z","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":"In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitemporal SAR image and its multilook version, in order to approximate a clean image. Experimental results, both on synthetic and real SAR data, show the method to achieve better performance with respect to state-of-the-art techniques.","authors_text":"D. Cozzolino, G. Chierchia, G. Poggi, L. Verdoliva","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-02T09:44:05Z","title":"SAR image despeckling through convolutional neural networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.00275","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":"sha256:0f53289927b91a45094cb0e88218313be29652185a3012215e7690e9f6f518f9","target":"record","created_at":"2026-05-18T00:45:06Z","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":"560a34a2ad56f3f3e5ccfb441202e8fe2f640cffd4f50fa1c71686cf52eb11ff","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-02T09:44:05Z","title_canon_sha256":"0d1b78e8e9adec20f1adfbe2ce855946a67a3766b35c3a01afbe0d56e231151e"},"schema_version":"1.0","source":{"id":"1704.00275","kind":"arxiv","version":2}},"canonical_sha256":"6f0153007cafe2920450581f034ae024c84c93fdaf9cbc35d58b797ae37d3d68","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6f0153007cafe2920450581f034ae024c84c93fdaf9cbc35d58b797ae37d3d68","first_computed_at":"2026-05-18T00:45:06.046371Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:45:06.046371Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"thUq2KZ+mECy79AIXNFdPAjtJIrxEemchYLYyr1Sqttb5fySWitTfZhBWU+LkcDlp3KIJWe5FLuGoBNowxGdDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:45:06.046731Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.00275","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0f53289927b91a45094cb0e88218313be29652185a3012215e7690e9f6f518f9","sha256:06aa99765852d95b85224fc9cfe14d02a4c3c2cc8bc3ada044882bef8adfde2a"],"state_sha256":"8200eb6e4912dfbaddb3313ef03bae47487ac44872d7164996e2613ce22722e6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CFY4/5BpySsHlTm48eAiamzPyPjQW/1zpFPT3i4HunJp1wTJisqmV2xZIeSbph88F/WHIUzTh6k7zCd6VpWyBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T13:44:46.691311Z","bundle_sha256":"60529463814989a1923590bb2a493918fd28fc7a12200edcad35518e76a4ad45"}}