{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:YXYXCQ2MLNSACMYMRFB5II2CE6","short_pith_number":"pith:YXYXCQ2M","schema_version":"1.0","canonical_sha256":"c5f171434c5b6401330c8943d4234227b3623b20d844e65739b1112901a92e6d","source":{"kind":"arxiv","id":"2010.11290","version":2},"attestation_state":"computed","paper":{"title":"Unrolling of Deep Graph Total Variation for Image Denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Gene Cheung, Huy Vu, Yonina C. Eldar","submitted_at":"2020-10-21T20:04:22Z","abstract_excerpt":"While deep learning (DL) architectures like convolutional neural networks (CNNs) have enabled effective solutions in image denoising, in general their implementations overly rely on training data, lack interpretability, and require tuning of a large parameter set. In this paper, we combine classical graph signal filtering with deep feature learning into a competitive hybrid design -- one that utilizes interpretable analytical low-pass graph filters and employs 80% fewer network parameters than state-of-the-art DL denoising scheme DnCNN. Specifically, to construct a suitable similarity graph fo"},"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":"2010.11290","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-10-21T20:04:22Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"b7380159843dd5a76d9b2b2b20ccf5945ac0f0fb195d3d397a26a6bb3cda369c","abstract_canon_sha256":"1e19fe6bc89b4eea6c710d4fce291a5b4596c29ab0589ea74c2de0f600d21b7e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:26:01.473210Z","signature_b64":"52rCb+dY8vtmnXk6mm0Qxg8V/wDUGPsC/yc2Mun62ZBOUxtC5jJOJjLiIozjKeJaOB29HbFqF+gh8CMLQb+7AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5f171434c5b6401330c8943d4234227b3623b20d844e65739b1112901a92e6d","last_reissued_at":"2026-07-05T02:26:01.472637Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:26:01.472637Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unrolling of Deep Graph Total Variation for Image Denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Gene Cheung, Huy Vu, Yonina C. Eldar","submitted_at":"2020-10-21T20:04:22Z","abstract_excerpt":"While deep learning (DL) architectures like convolutional neural networks (CNNs) have enabled effective solutions in image denoising, in general their implementations overly rely on training data, lack interpretability, and require tuning of a large parameter set. In this paper, we combine classical graph signal filtering with deep feature learning into a competitive hybrid design -- one that utilizes interpretable analytical low-pass graph filters and employs 80% fewer network parameters than state-of-the-art DL denoising scheme DnCNN. Specifically, to construct a suitable similarity graph fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.11290","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2010.11290/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":"2010.11290","created_at":"2026-07-05T02:26:01.472706+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.11290v2","created_at":"2026-07-05T02:26:01.472706+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.11290","created_at":"2026-07-05T02:26:01.472706+00:00"},{"alias_kind":"pith_short_12","alias_value":"YXYXCQ2MLNSA","created_at":"2026-07-05T02:26:01.472706+00:00"},{"alias_kind":"pith_short_16","alias_value":"YXYXCQ2MLNSACMYM","created_at":"2026-07-05T02:26:01.472706+00:00"},{"alias_kind":"pith_short_8","alias_value":"YXYXCQ2M","created_at":"2026-07-05T02:26:01.472706+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/YXYXCQ2MLNSACMYMRFB5II2CE6","json":"https://pith.science/pith/YXYXCQ2MLNSACMYMRFB5II2CE6.json","graph_json":"https://pith.science/api/pith-number/YXYXCQ2MLNSACMYMRFB5II2CE6/graph.json","events_json":"https://pith.science/api/pith-number/YXYXCQ2MLNSACMYMRFB5II2CE6/events.json","paper":"https://pith.science/paper/YXYXCQ2M"},"agent_actions":{"view_html":"https://pith.science/pith/YXYXCQ2MLNSACMYMRFB5II2CE6","download_json":"https://pith.science/pith/YXYXCQ2MLNSACMYMRFB5II2CE6.json","view_paper":"https://pith.science/paper/YXYXCQ2M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.11290&json=true","fetch_graph":"https://pith.science/api/pith-number/YXYXCQ2MLNSACMYMRFB5II2CE6/graph.json","fetch_events":"https://pith.science/api/pith-number/YXYXCQ2MLNSACMYMRFB5II2CE6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YXYXCQ2MLNSACMYMRFB5II2CE6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YXYXCQ2MLNSACMYMRFB5II2CE6/action/storage_attestation","attest_author":"https://pith.science/pith/YXYXCQ2MLNSACMYMRFB5II2CE6/action/author_attestation","sign_citation":"https://pith.science/pith/YXYXCQ2MLNSACMYMRFB5II2CE6/action/citation_signature","submit_replication":"https://pith.science/pith/YXYXCQ2MLNSACMYMRFB5II2CE6/action/replication_record"}},"created_at":"2026-07-05T02:26:01.472706+00:00","updated_at":"2026-07-05T02:26:01.472706+00:00"}