{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:JLDMY5EPYURHCWSK4HIJX3US4R","short_pith_number":"pith:JLDMY5EP","canonical_record":{"source":{"id":"2203.02166","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-03-04T07:32:16Z","cross_cats_sorted":["cs.CV","eess.SP"],"title_canon_sha256":"329f4c5d9f9634643247e21e605031557851fd1230651cca542dc4186d0b55e9","abstract_canon_sha256":"eaac5e81f394747ce188d7bd8ea2037d796a7b4a6eb7b541d6a0d92102236eed"},"schema_version":"1.0"},"canonical_sha256":"4ac6cc748fc522715a4ae1d09bee92e4659676161fb8c8e7ffa856e6960fd294","source":{"kind":"arxiv","id":"2203.02166","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.02166","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"arxiv_version","alias_value":"2203.02166v1","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.02166","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"pith_short_12","alias_value":"JLDMY5EPYURH","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"pith_short_16","alias_value":"JLDMY5EPYURHCWSK","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"pith_short_8","alias_value":"JLDMY5EP","created_at":"2026-07-05T04:02:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:JLDMY5EPYURHCWSK4HIJX3US4R","target":"record","payload":{"canonical_record":{"source":{"id":"2203.02166","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-03-04T07:32:16Z","cross_cats_sorted":["cs.CV","eess.SP"],"title_canon_sha256":"329f4c5d9f9634643247e21e605031557851fd1230651cca542dc4186d0b55e9","abstract_canon_sha256":"eaac5e81f394747ce188d7bd8ea2037d796a7b4a6eb7b541d6a0d92102236eed"},"schema_version":"1.0"},"canonical_sha256":"4ac6cc748fc522715a4ae1d09bee92e4659676161fb8c8e7ffa856e6960fd294","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:02:01.760935Z","signature_b64":"Zd0U8nfZtnYrESEVYHvSTsWBDwjtx0VM/RNnStZvHV4I1j55pYstA/vw3IB9sSoXpXA0sMT+MryQSayE0U4aBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ac6cc748fc522715a4ae1d09bee92e4659676161fb8c8e7ffa856e6960fd294","last_reissued_at":"2026-07-05T04:02:01.760511Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:02:01.760511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.02166","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":"2026-07-05T04:02:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VQQINPwDqF64fxGu51yTTrUb7Dg5vin0NfDKMtq0WN+XOAfLhOThZhXP9Y0zAMbwHgIZStkS2uqcmbUn9blFBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T22:54:46.877944Z"},"content_sha256":"6f2c5cdd2ab9bfde5682bad8b5c1953fd1d65d48babf75613c3ac5138405b661","schema_version":"1.0","event_id":"sha256:6f2c5cdd2ab9bfde5682bad8b5c1953fd1d65d48babf75613c3ac5138405b661"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:JLDMY5EPYURHCWSK4HIJX3US4R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","eess.SP"],"primary_cat":"eess.IV","authors_text":"Andreas Kofler, Christian Wald, Christoph Kolbitsch, Markus Haltmeier, Tobias Schaeffter","submitted_at":"2022-03-04T07:32:16Z","abstract_excerpt":"The concept of sparsity has been extensively applied for regularization in image reconstruction. Typically, sparsifying transforms are either pre-trained on ground-truth images or adaptively trained during the reconstruction. Thereby, learning algorithms are designed to minimize some target function which encodes the desired properties of the transform. However, this procedure ignores the subsequently employed reconstruction algorithm as well as the physical model which is responsible for the image formation process. Iterative neural networks - which contain the physical model - can overcome t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.02166","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2203.02166/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T04:02:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UuMZUcA1hJYeoDzX5D6TMBVgPouYHxD3vSkyCmWe4QmM1qG7qeXMGp9DrOw+HUgIqkL+E2r/iGZkkFsGsrQtDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T22:54:46.878913Z"},"content_sha256":"2e0fd6edd6d476f892d706cdd7ab11dfee3295ef7b94d03033cc7aac4763b6ab","schema_version":"1.0","event_id":"sha256:2e0fd6edd6d476f892d706cdd7ab11dfee3295ef7b94d03033cc7aac4763b6ab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JLDMY5EPYURHCWSK4HIJX3US4R/bundle.json","state_url":"https://pith.science/pith/JLDMY5EPYURHCWSK4HIJX3US4R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JLDMY5EPYURHCWSK4HIJX3US4R/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":"2026-08-06T22:54:46Z","links":{"resolver":"https://pith.science/pith/JLDMY5EPYURHCWSK4HIJX3US4R","bundle":"https://pith.science/pith/JLDMY5EPYURHCWSK4HIJX3US4R/bundle.json","state":"https://pith.science/pith/JLDMY5EPYURHCWSK4HIJX3US4R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JLDMY5EPYURHCWSK4HIJX3US4R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:JLDMY5EPYURHCWSK4HIJX3US4R","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":"eaac5e81f394747ce188d7bd8ea2037d796a7b4a6eb7b541d6a0d92102236eed","cross_cats_sorted":["cs.CV","eess.SP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-03-04T07:32:16Z","title_canon_sha256":"329f4c5d9f9634643247e21e605031557851fd1230651cca542dc4186d0b55e9"},"schema_version":"1.0","source":{"id":"2203.02166","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.02166","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"arxiv_version","alias_value":"2203.02166v1","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.02166","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"pith_short_12","alias_value":"JLDMY5EPYURH","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"pith_short_16","alias_value":"JLDMY5EPYURHCWSK","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"pith_short_8","alias_value":"JLDMY5EP","created_at":"2026-07-05T04:02:01Z"}],"graph_snapshots":[{"event_id":"sha256:2e0fd6edd6d476f892d706cdd7ab11dfee3295ef7b94d03033cc7aac4763b6ab","target":"graph","created_at":"2026-07-05T04:02:01Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2203.02166/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The concept of sparsity has been extensively applied for regularization in image reconstruction. Typically, sparsifying transforms are either pre-trained on ground-truth images or adaptively trained during the reconstruction. Thereby, learning algorithms are designed to minimize some target function which encodes the desired properties of the transform. However, this procedure ignores the subsequently employed reconstruction algorithm as well as the physical model which is responsible for the image formation process. Iterative neural networks - which contain the physical model - can overcome t","authors_text":"Andreas Kofler, Christian Wald, Christoph Kolbitsch, Markus Haltmeier, Tobias Schaeffter","cross_cats":["cs.CV","eess.SP"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-03-04T07:32:16Z","title":"Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.02166","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":"sha256:6f2c5cdd2ab9bfde5682bad8b5c1953fd1d65d48babf75613c3ac5138405b661","target":"record","created_at":"2026-07-05T04:02:01Z","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":"eaac5e81f394747ce188d7bd8ea2037d796a7b4a6eb7b541d6a0d92102236eed","cross_cats_sorted":["cs.CV","eess.SP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-03-04T07:32:16Z","title_canon_sha256":"329f4c5d9f9634643247e21e605031557851fd1230651cca542dc4186d0b55e9"},"schema_version":"1.0","source":{"id":"2203.02166","kind":"arxiv","version":1}},"canonical_sha256":"4ac6cc748fc522715a4ae1d09bee92e4659676161fb8c8e7ffa856e6960fd294","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4ac6cc748fc522715a4ae1d09bee92e4659676161fb8c8e7ffa856e6960fd294","first_computed_at":"2026-07-05T04:02:01.760511Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:02:01.760511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Zd0U8nfZtnYrESEVYHvSTsWBDwjtx0VM/RNnStZvHV4I1j55pYstA/vw3IB9sSoXpXA0sMT+MryQSayE0U4aBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:02:01.760935Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.02166","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6f2c5cdd2ab9bfde5682bad8b5c1953fd1d65d48babf75613c3ac5138405b661","sha256:2e0fd6edd6d476f892d706cdd7ab11dfee3295ef7b94d03033cc7aac4763b6ab"],"state_sha256":"89cb2d56bed9a769036ef05f1bb4b371ad1b1b2b70f1171a76ae553c1336ab87"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gV5lwmz5rQsz7i3dkMn9YKXetneBwp4CJw0JM4F73YSfHZ/k4C3sqD145kh7jum5DeJy18w5MZyVDru5M9ENBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T22:54:46.884756Z","bundle_sha256":"0b96b7ddc24d51f3a7c1e17ac63693a57f85e28736a6be56037ea949d4863e7f"}}