{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:RXJO4NCNERK4WIX4DNBWM2N26G","short_pith_number":"pith:RXJO4NCN","schema_version":"1.0","canonical_sha256":"8dd2ee344d2455cb22fc1b436669baf1a862765db3cd7a58c4cfff16df62053c","source":{"kind":"arxiv","id":"1607.04433","version":2},"attestation_state":"computed","paper":{"title":"End-to-End Learning for Image Burst Deblurring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bernhard Sch\\\"olkopf, Hendrik P.A. Lensch, Michael Hirsch, Patrick Wieschollek","submitted_at":"2016-07-15T09:46:49Z","abstract_excerpt":"We present a neural network model approach for multi-frame blind deconvolution. The discriminative approach adopts and combines two recent techniques for image deblurring into a single neural network architecture. Our proposed hybrid-architecture combines the explicit prediction of a deconvolution filter and non-trivial averaging of Fourier coefficients in the frequency domain. In order to make full use of the information contained in all images in one burst, the proposed network embeds smaller networks, which explicitly allow the model to transfer information between images in early layers. O"},"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":"1607.04433","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-15T09:46:49Z","cross_cats_sorted":[],"title_canon_sha256":"32342ea8a2c56bfec046f842ad2d76f5abc357fdba89d0dbacf94bfc0b219e3e","abstract_canon_sha256":"5b4089643fd4da2f04cb7dec78f101940f16c2b976576f6f0ffcb4d3bfa22ec4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:37.895440Z","signature_b64":"7y35zF/lTV5YB7owo6QrUCN28/KA6kAd6azeuS7dVMA7lDj6pwUZEQVuphdz1sB7x7vcTsX1f7tKNdPbeFNiBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8dd2ee344d2455cb22fc1b436669baf1a862765db3cd7a58c4cfff16df62053c","last_reissued_at":"2026-05-18T00:49:37.894692Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:37.894692Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"End-to-End Learning for Image Burst Deblurring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bernhard Sch\\\"olkopf, Hendrik P.A. Lensch, Michael Hirsch, Patrick Wieschollek","submitted_at":"2016-07-15T09:46:49Z","abstract_excerpt":"We present a neural network model approach for multi-frame blind deconvolution. The discriminative approach adopts and combines two recent techniques for image deblurring into a single neural network architecture. Our proposed hybrid-architecture combines the explicit prediction of a deconvolution filter and non-trivial averaging of Fourier coefficients in the frequency domain. In order to make full use of the information contained in all images in one burst, the proposed network embeds smaller networks, which explicitly allow the model to transfer information between images in early layers. O"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.04433","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1607.04433","created_at":"2026-05-18T00:49:37.894805+00:00"},{"alias_kind":"arxiv_version","alias_value":"1607.04433v2","created_at":"2026-05-18T00:49:37.894805+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.04433","created_at":"2026-05-18T00:49:37.894805+00:00"},{"alias_kind":"pith_short_12","alias_value":"RXJO4NCNERK4","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"RXJO4NCNERK4WIX4","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"RXJO4NCN","created_at":"2026-05-18T12:30:41.710351+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/RXJO4NCNERK4WIX4DNBWM2N26G","json":"https://pith.science/pith/RXJO4NCNERK4WIX4DNBWM2N26G.json","graph_json":"https://pith.science/api/pith-number/RXJO4NCNERK4WIX4DNBWM2N26G/graph.json","events_json":"https://pith.science/api/pith-number/RXJO4NCNERK4WIX4DNBWM2N26G/events.json","paper":"https://pith.science/paper/RXJO4NCN"},"agent_actions":{"view_html":"https://pith.science/pith/RXJO4NCNERK4WIX4DNBWM2N26G","download_json":"https://pith.science/pith/RXJO4NCNERK4WIX4DNBWM2N26G.json","view_paper":"https://pith.science/paper/RXJO4NCN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1607.04433&json=true","fetch_graph":"https://pith.science/api/pith-number/RXJO4NCNERK4WIX4DNBWM2N26G/graph.json","fetch_events":"https://pith.science/api/pith-number/RXJO4NCNERK4WIX4DNBWM2N26G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RXJO4NCNERK4WIX4DNBWM2N26G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RXJO4NCNERK4WIX4DNBWM2N26G/action/storage_attestation","attest_author":"https://pith.science/pith/RXJO4NCNERK4WIX4DNBWM2N26G/action/author_attestation","sign_citation":"https://pith.science/pith/RXJO4NCNERK4WIX4DNBWM2N26G/action/citation_signature","submit_replication":"https://pith.science/pith/RXJO4NCNERK4WIX4DNBWM2N26G/action/replication_record"}},"created_at":"2026-05-18T00:49:37.894805+00:00","updated_at":"2026-05-18T00:49:37.894805+00:00"}