{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:V6UHLHEY3BI3JAAXYXZRLDXK43","short_pith_number":"pith:V6UHLHEY","schema_version":"1.0","canonical_sha256":"afa8759c98d851b48017c5f3158eeae6d10bcd8b6f862d355f134099a89cd7c5","source":{"kind":"arxiv","id":"1706.04284","version":3},"attestation_state":"computed","paper":{"title":"When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bihan Wen, Ding Liu, Thomas S. Huang, Xianming Liu, Zhangyang Wang","submitted_at":"2017-06-14T00:04:56Z","abstract_excerpt":"Conventionally, image denoising and high-level vision tasks are handled separately in computer vision. In this paper, we cope with the two jointly and explore the mutual influence between them. First we propose a convolutional neural network for image denoising which achieves the state-of-the-art performance. Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation. We demonstrate that on one hand, the proposed denoiser has the ge"},"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":"1706.04284","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-14T00:04:56Z","cross_cats_sorted":[],"title_canon_sha256":"04150ea8a33ffcec09bf890d7fee042836f226fa4a0e2a4d942e7643aee9a7fe","abstract_canon_sha256":"425c700d40a13754e8dc0ce897eb3323c6fb466f4ac76b506d569c7beb416ee2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:25.779822Z","signature_b64":"yraKe5p2zhdAE85nH7urBfb43NdCSRsqTMls14I4R0WNyN0TXWfM82jxlkQJHPsQ5I8UYnqqejJ5H/qo4x8uDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"afa8759c98d851b48017c5f3158eeae6d10bcd8b6f862d355f134099a89cd7c5","last_reissued_at":"2026-05-18T00:18:25.779348Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:25.779348Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bihan Wen, Ding Liu, Thomas S. Huang, Xianming Liu, Zhangyang Wang","submitted_at":"2017-06-14T00:04:56Z","abstract_excerpt":"Conventionally, image denoising and high-level vision tasks are handled separately in computer vision. In this paper, we cope with the two jointly and explore the mutual influence between them. First we propose a convolutional neural network for image denoising which achieves the state-of-the-art performance. Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation. We demonstrate that on one hand, the proposed denoiser has the ge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.04284","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":""},"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":"1706.04284","created_at":"2026-05-18T00:18:25.779420+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.04284v3","created_at":"2026-05-18T00:18:25.779420+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.04284","created_at":"2026-05-18T00:18:25.779420+00:00"},{"alias_kind":"pith_short_12","alias_value":"V6UHLHEY3BI3","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_16","alias_value":"V6UHLHEY3BI3JAAX","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_8","alias_value":"V6UHLHEY","created_at":"2026-05-18T12:31:49.984773+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.21964","citing_title":"Dual-Integrated Low-Latency Single-Lens Infrared Computational Imaging for Object Detection","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V6UHLHEY3BI3JAAXYXZRLDXK43","json":"https://pith.science/pith/V6UHLHEY3BI3JAAXYXZRLDXK43.json","graph_json":"https://pith.science/api/pith-number/V6UHLHEY3BI3JAAXYXZRLDXK43/graph.json","events_json":"https://pith.science/api/pith-number/V6UHLHEY3BI3JAAXYXZRLDXK43/events.json","paper":"https://pith.science/paper/V6UHLHEY"},"agent_actions":{"view_html":"https://pith.science/pith/V6UHLHEY3BI3JAAXYXZRLDXK43","download_json":"https://pith.science/pith/V6UHLHEY3BI3JAAXYXZRLDXK43.json","view_paper":"https://pith.science/paper/V6UHLHEY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.04284&json=true","fetch_graph":"https://pith.science/api/pith-number/V6UHLHEY3BI3JAAXYXZRLDXK43/graph.json","fetch_events":"https://pith.science/api/pith-number/V6UHLHEY3BI3JAAXYXZRLDXK43/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V6UHLHEY3BI3JAAXYXZRLDXK43/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V6UHLHEY3BI3JAAXYXZRLDXK43/action/storage_attestation","attest_author":"https://pith.science/pith/V6UHLHEY3BI3JAAXYXZRLDXK43/action/author_attestation","sign_citation":"https://pith.science/pith/V6UHLHEY3BI3JAAXYXZRLDXK43/action/citation_signature","submit_replication":"https://pith.science/pith/V6UHLHEY3BI3JAAXYXZRLDXK43/action/replication_record"}},"created_at":"2026-05-18T00:18:25.779420+00:00","updated_at":"2026-05-18T00:18:25.779420+00:00"}