{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QUQBEY3JIWRPYWUT3P7FFF25GX","short_pith_number":"pith:QUQBEY3J","schema_version":"1.0","canonical_sha256":"852012636945a2fc5a93dbfe52975d35e716599d756fa516a3a0e8d30ed2cb8a","source":{"kind":"arxiv","id":"2403.06069","version":3},"attestation_state":"computed","paper":{"title":"Implicit Image-to-Image Schrodinger Bridge for Image Restoration","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Dufan Wu, Li Zhang, Matthew Tivnan, Pengfei Jin, Quanzheng Li, Rui Hu, Sifan Song, Siyeop Yoon, Yuang Wang, Zhennong Chen, Zhiqiang Chen","submitted_at":"2024-03-10T03:22:57Z","abstract_excerpt":"Diffusion-based models have demonstrated remarkable effectiveness in image restoration tasks; however, their iterative denoising process, which starts from Gaussian noise, often leads to slow inference speeds. The Image-to-Image Schr\\\"odinger Bridge (I$^2$SB) offers a promising alternative by initializing the generative process from corrupted images while leveraging training techniques from score-based diffusion models. In this paper, we introduce the Implicit Image-to-Image Schr\\\"odinger Bridge (I$^3$SB) to further accelerate the generative process of I$^2$SB. I$^3$SB restructures the generat"},"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":"2403.06069","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-03-10T03:22:57Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"60c7b40542d2e679438fe15034333a72682ae0d822052c634ac4cf70b09da187","abstract_canon_sha256":"4ba2ac6d4036a33433168257c6fa4ef8a83e134b5abc174d3480cf1e75563e0c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:37:24.724338Z","signature_b64":"DCIL3TXJ3+QmGJo4Xk7VADNWO8KUbR/ZhoHbTahgISAfiT6FQ1bfK0yO3HYAdIMS46m+K85BqqgmhQN8tpCPCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"852012636945a2fc5a93dbfe52975d35e716599d756fa516a3a0e8d30ed2cb8a","last_reissued_at":"2026-07-05T10:37:24.723286Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:37:24.723286Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Implicit Image-to-Image Schrodinger Bridge for Image Restoration","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Dufan Wu, Li Zhang, Matthew Tivnan, Pengfei Jin, Quanzheng Li, Rui Hu, Sifan Song, Siyeop Yoon, Yuang Wang, Zhennong Chen, Zhiqiang Chen","submitted_at":"2024-03-10T03:22:57Z","abstract_excerpt":"Diffusion-based models have demonstrated remarkable effectiveness in image restoration tasks; however, their iterative denoising process, which starts from Gaussian noise, often leads to slow inference speeds. The Image-to-Image Schr\\\"odinger Bridge (I$^2$SB) offers a promising alternative by initializing the generative process from corrupted images while leveraging training techniques from score-based diffusion models. In this paper, we introduce the Implicit Image-to-Image Schr\\\"odinger Bridge (I$^3$SB) to further accelerate the generative process of I$^2$SB. I$^3$SB restructures the generat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.06069","kind":"arxiv","version":3},"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/2403.06069/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":"2403.06069","created_at":"2026-07-05T10:37:24.723418+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.06069v3","created_at":"2026-07-05T10:37:24.723418+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.06069","created_at":"2026-07-05T10:37:24.723418+00:00"},{"alias_kind":"pith_short_12","alias_value":"QUQBEY3JIWRP","created_at":"2026-07-05T10:37:24.723418+00:00"},{"alias_kind":"pith_short_16","alias_value":"QUQBEY3JIWRPYWUT","created_at":"2026-07-05T10:37:24.723418+00:00"},{"alias_kind":"pith_short_8","alias_value":"QUQBEY3J","created_at":"2026-07-05T10:37:24.723418+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.19992","citing_title":"An Ordinary Differential Equation Sampler with Stochastic Start for Diffusion Bridge Models","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QUQBEY3JIWRPYWUT3P7FFF25GX","json":"https://pith.science/pith/QUQBEY3JIWRPYWUT3P7FFF25GX.json","graph_json":"https://pith.science/api/pith-number/QUQBEY3JIWRPYWUT3P7FFF25GX/graph.json","events_json":"https://pith.science/api/pith-number/QUQBEY3JIWRPYWUT3P7FFF25GX/events.json","paper":"https://pith.science/paper/QUQBEY3J"},"agent_actions":{"view_html":"https://pith.science/pith/QUQBEY3JIWRPYWUT3P7FFF25GX","download_json":"https://pith.science/pith/QUQBEY3JIWRPYWUT3P7FFF25GX.json","view_paper":"https://pith.science/paper/QUQBEY3J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.06069&json=true","fetch_graph":"https://pith.science/api/pith-number/QUQBEY3JIWRPYWUT3P7FFF25GX/graph.json","fetch_events":"https://pith.science/api/pith-number/QUQBEY3JIWRPYWUT3P7FFF25GX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QUQBEY3JIWRPYWUT3P7FFF25GX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QUQBEY3JIWRPYWUT3P7FFF25GX/action/storage_attestation","attest_author":"https://pith.science/pith/QUQBEY3JIWRPYWUT3P7FFF25GX/action/author_attestation","sign_citation":"https://pith.science/pith/QUQBEY3JIWRPYWUT3P7FFF25GX/action/citation_signature","submit_replication":"https://pith.science/pith/QUQBEY3JIWRPYWUT3P7FFF25GX/action/replication_record"}},"created_at":"2026-07-05T10:37:24.723418+00:00","updated_at":"2026-07-05T10:37:24.723418+00:00"}