{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:R3WBXESRPTAYLYPMWKJVCWNYXG","short_pith_number":"pith:R3WBXESR","schema_version":"1.0","canonical_sha256":"8eec1b92517cc185e1ecb2935159b8b9a5bbcc278e37e29017db66aa8304f02c","source":{"kind":"arxiv","id":"2409.17058","version":1},"attestation_state":"computed","paper":{"title":"Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aiping Zhang, Renjing Pei, Wenqi Ren, Xiaochun Cao, Zongsheng Yue","submitted_at":"2024-09-25T16:15:21Z","abstract_excerpt":"Diffusion-based image super-resolution (SR) methods have achieved remarkable success by leveraging large pre-trained text-to-image diffusion models as priors. However, these methods still face two challenges: the requirement for dozens of sampling steps to achieve satisfactory results, which limits efficiency in real scenarios, and the neglect of degradation models, which are critical auxiliary information in solving the SR problem. In this work, we introduced a novel one-step SR model, which significantly addresses the efficiency issue of diffusion-based SR methods. Unlike existing fine-tunin"},"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":"2409.17058","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-25T16:15:21Z","cross_cats_sorted":[],"title_canon_sha256":"eeac68db816d29983bd2b1488e846229533b29bbc3e511267023212d8081cf40","abstract_canon_sha256":"5ad84508bdc06d4bb99f0ed18b0806719967d73b2ec977d103fac04c3e1ec271"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:11:43.281343Z","signature_b64":"RCtKGfPCmTX55KE9cEG56Iq519AVOrMdQ/nJsU+wXIwZrSXkZNlUdmuzP5VWQRoSCobDIPHtvq5mn1M7BFxPCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8eec1b92517cc185e1ecb2935159b8b9a5bbcc278e37e29017db66aa8304f02c","last_reissued_at":"2026-07-05T09:11:43.280895Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:11:43.280895Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aiping Zhang, Renjing Pei, Wenqi Ren, Xiaochun Cao, Zongsheng Yue","submitted_at":"2024-09-25T16:15:21Z","abstract_excerpt":"Diffusion-based image super-resolution (SR) methods have achieved remarkable success by leveraging large pre-trained text-to-image diffusion models as priors. However, these methods still face two challenges: the requirement for dozens of sampling steps to achieve satisfactory results, which limits efficiency in real scenarios, and the neglect of degradation models, which are critical auxiliary information in solving the SR problem. In this work, we introduced a novel one-step SR model, which significantly addresses the efficiency issue of diffusion-based SR methods. Unlike existing fine-tunin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.17058","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/2409.17058/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":"2409.17058","created_at":"2026-07-05T09:11:43.280951+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.17058v1","created_at":"2026-07-05T09:11:43.280951+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.17058","created_at":"2026-07-05T09:11:43.280951+00:00"},{"alias_kind":"pith_short_12","alias_value":"R3WBXESRPTAY","created_at":"2026-07-05T09:11:43.280951+00:00"},{"alias_kind":"pith_short_16","alias_value":"R3WBXESRPTAYLYPM","created_at":"2026-07-05T09:11:43.280951+00:00"},{"alias_kind":"pith_short_8","alias_value":"R3WBXESR","created_at":"2026-07-05T09:11:43.280951+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":16,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.12377","citing_title":"Fast Image Super-Resolution via Consistency Rectified Flow","ref_index":64,"is_internal_anchor":false},{"citing_arxiv_id":"2605.23451","citing_title":"Efficient One-Step Diffusion Restoration Model with Compact Token Compression and Linear Attention","ref_index":51,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02198","citing_title":"SlimDiffSR: Toward Lightweight and Efficient Remote Sensing Image Super-Resolution via Diffusion Model Distillation","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15682","citing_title":"DreamSR: Towards Ultra-High-Resolution Image Super-Resolution via a Receptive-Field Enhanced Diffusion Transformer","ref_index":71,"is_internal_anchor":false},{"citing_arxiv_id":"2505.18600","citing_title":"Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment","ref_index":56,"is_internal_anchor":false},{"citing_arxiv_id":"2603.16570","citing_title":"Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration","ref_index":60,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13457","citing_title":"OP4KSR: One-Step Patch-Free 4K Super-Resolution with Periodic Artifact Suppression","ref_index":67,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12377","citing_title":"Fast Image Super-Resolution via Consistency Rectified Flow","ref_index":64,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07429","citing_title":"Towards Photorealistic and Efficient Bokeh Rendering via Diffusion Framework","ref_index":59,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25457","citing_title":"GramSR: Visual Feature Conditioning for Diffusion-Based Super-Resolution","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23508","citing_title":"BurstGP: Enhancing Raw Burst Image Super Resolution with Generative Priors","ref_index":75,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02198","citing_title":"SlimDiffSR: Toward Lightweight and Efficient Remote Sensing Image Super-Resolution via Diffusion Model Distillation","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00605","citing_title":"Faithful Extreme Image Rescaling with Learnable Reversible Transformation and Semantic Priors","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07429","citing_title":"Towards Photorealistic and Efficient Bokeh Rendering via Diffusion Framework","ref_index":59,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16858","citing_title":"Q-DeepSight: Incentivizing Thinking with Images for Image Quality Assessment and Refinement","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02767","citing_title":"TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R3WBXESRPTAYLYPMWKJVCWNYXG","json":"https://pith.science/pith/R3WBXESRPTAYLYPMWKJVCWNYXG.json","graph_json":"https://pith.science/api/pith-number/R3WBXESRPTAYLYPMWKJVCWNYXG/graph.json","events_json":"https://pith.science/api/pith-number/R3WBXESRPTAYLYPMWKJVCWNYXG/events.json","paper":"https://pith.science/paper/R3WBXESR"},"agent_actions":{"view_html":"https://pith.science/pith/R3WBXESRPTAYLYPMWKJVCWNYXG","download_json":"https://pith.science/pith/R3WBXESRPTAYLYPMWKJVCWNYXG.json","view_paper":"https://pith.science/paper/R3WBXESR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.17058&json=true","fetch_graph":"https://pith.science/api/pith-number/R3WBXESRPTAYLYPMWKJVCWNYXG/graph.json","fetch_events":"https://pith.science/api/pith-number/R3WBXESRPTAYLYPMWKJVCWNYXG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R3WBXESRPTAYLYPMWKJVCWNYXG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R3WBXESRPTAYLYPMWKJVCWNYXG/action/storage_attestation","attest_author":"https://pith.science/pith/R3WBXESRPTAYLYPMWKJVCWNYXG/action/author_attestation","sign_citation":"https://pith.science/pith/R3WBXESRPTAYLYPMWKJVCWNYXG/action/citation_signature","submit_replication":"https://pith.science/pith/R3WBXESRPTAYLYPMWKJVCWNYXG/action/replication_record"}},"created_at":"2026-07-05T09:11:43.280951+00:00","updated_at":"2026-07-05T09:11:43.280951+00:00"}