{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LSZJBHBZGREEIKR2JS275XQFNG","short_pith_number":"pith:LSZJBHBZ","schema_version":"1.0","canonical_sha256":"5cb2909c393448442a3a4cb5fede056990949d3937328062a56ceea7453bda6d","source":{"kind":"arxiv","id":"2606.06540","version":1},"attestation_state":"computed","paper":{"title":"ErA: Error-Aware Deep Unrolling Network for Single Image Defocus Deblurring","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Chan Y. Park, Tu Vo","submitted_at":"2026-06-04T04:19:08Z","abstract_excerpt":"We introduce ErA (Error-Aware Deep Unrolling Network), an end-to-end frame work for single-image defocus deblurring. ErA jointly learns a compact kerne basis and per-pixel weights, while an error-aware term in Augmented Lagrangian unrolling corrects kernel estimation errors via alternating updates and ResUNet denoisers. It achieves state-of-the-art PSNR/SSIM on DPDD, RealDOF, and RTF, and shows strong generalization on CUHK without ground truth."},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.06540","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-06-04T04:19:08Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"4a3ca07a5fffc371239cae13d34d5450ee51b19154056a6a9af8de04de13dab8","abstract_canon_sha256":"cff938fb33d498149786c0bdd816e8ba8e52ab87e4ffa95d4435d52abfb7e6e3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T00:03:41.651709Z","signature_b64":"CXYPO7MEfXyrwqKFWgM51MLqrZXsMkeZFFQP3kB6eaoP/skDuk4n9ecsGW0ALUKRRe3wVpw5JVnIImpbT2GeBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5cb2909c393448442a3a4cb5fede056990949d3937328062a56ceea7453bda6d","last_reissued_at":"2026-06-08T00:03:41.651227Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T00:03:41.651227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ErA: Error-Aware Deep Unrolling Network for Single Image Defocus Deblurring","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Chan Y. Park, Tu Vo","submitted_at":"2026-06-04T04:19:08Z","abstract_excerpt":"We introduce ErA (Error-Aware Deep Unrolling Network), an end-to-end frame work for single-image defocus deblurring. ErA jointly learns a compact kerne basis and per-pixel weights, while an error-aware term in Augmented Lagrangian unrolling corrects kernel estimation errors via alternating updates and ResUNet denoisers. It achieves state-of-the-art PSNR/SSIM on DPDD, RealDOF, and RTF, and shows strong generalization on CUHK without ground truth."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06540","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/2606.06540/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.06540","created_at":"2026-06-08T00:03:41.651289+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.06540v1","created_at":"2026-06-08T00:03:41.651289+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06540","created_at":"2026-06-08T00:03:41.651289+00:00"},{"alias_kind":"pith_short_12","alias_value":"LSZJBHBZGREE","created_at":"2026-06-08T00:03:41.651289+00:00"},{"alias_kind":"pith_short_16","alias_value":"LSZJBHBZGREEIKR2","created_at":"2026-06-08T00:03:41.651289+00:00"},{"alias_kind":"pith_short_8","alias_value":"LSZJBHBZ","created_at":"2026-06-08T00:03:41.651289+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LSZJBHBZGREEIKR2JS275XQFNG","json":"https://pith.science/pith/LSZJBHBZGREEIKR2JS275XQFNG.json","graph_json":"https://pith.science/api/pith-number/LSZJBHBZGREEIKR2JS275XQFNG/graph.json","events_json":"https://pith.science/api/pith-number/LSZJBHBZGREEIKR2JS275XQFNG/events.json","paper":"https://pith.science/paper/LSZJBHBZ"},"agent_actions":{"view_html":"https://pith.science/pith/LSZJBHBZGREEIKR2JS275XQFNG","download_json":"https://pith.science/pith/LSZJBHBZGREEIKR2JS275XQFNG.json","view_paper":"https://pith.science/paper/LSZJBHBZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.06540&json=true","fetch_graph":"https://pith.science/api/pith-number/LSZJBHBZGREEIKR2JS275XQFNG/graph.json","fetch_events":"https://pith.science/api/pith-number/LSZJBHBZGREEIKR2JS275XQFNG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LSZJBHBZGREEIKR2JS275XQFNG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LSZJBHBZGREEIKR2JS275XQFNG/action/storage_attestation","attest_author":"https://pith.science/pith/LSZJBHBZGREEIKR2JS275XQFNG/action/author_attestation","sign_citation":"https://pith.science/pith/LSZJBHBZGREEIKR2JS275XQFNG/action/citation_signature","submit_replication":"https://pith.science/pith/LSZJBHBZGREEIKR2JS275XQFNG/action/replication_record"}},"created_at":"2026-06-08T00:03:41.651289+00:00","updated_at":"2026-06-08T00:03:41.651289+00:00"}