{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:WG2AOBGVI73OZCNS7AD6C2GIEO","short_pith_number":"pith:WG2AOBGV","schema_version":"1.0","canonical_sha256":"b1b40704d547f6ec89b2f807e168c823b800e73594955801a02f7a4b8c609f02","source":{"kind":"arxiv","id":"2311.16845","version":1},"attestation_state":"computed","paper":{"title":"Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengwei Hu, Chenyu Dong, Chen Zhao, Weiling Cai","submitted_at":"2023-11-28T14:58:32Z","abstract_excerpt":"Underwater images are subject to intricate and diverse degradation, inevitably affecting the effectiveness of underwater visual tasks. However, most approaches primarily operate in the raw pixel space of images, which limits the exploration of the frequency characteristics of underwater images, leading to an inadequate utilization of deep models' representational capabilities in producing high-quality images. In this paper, we introduce a novel Underwater Image Enhancement (UIE) framework, named WF-Diff, designed to fully leverage the characteristics of frequency domain information and diffusi"},"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":"2311.16845","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-28T14:58:32Z","cross_cats_sorted":[],"title_canon_sha256":"8144cf3f7e95ad0b318d4327892c58fe5a4fc9da5ad3ff210704585d015e405a","abstract_canon_sha256":"346cbdc37077b78f1e2c7d6da802c6986c6451ac11bf054026552b6d031e8174"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:17:47.309431Z","signature_b64":"BfxJOS8r/6W6P8DEIZTuG13W+088+c6hXuWul5CSoAtbeispqg/PuK5AEyUqrwDSWXMAY1XUDzzmd2ifg42cDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1b40704d547f6ec89b2f807e168c823b800e73594955801a02f7a4b8c609f02","last_reissued_at":"2026-07-05T07:17:47.308858Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:17:47.308858Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengwei Hu, Chenyu Dong, Chen Zhao, Weiling Cai","submitted_at":"2023-11-28T14:58:32Z","abstract_excerpt":"Underwater images are subject to intricate and diverse degradation, inevitably affecting the effectiveness of underwater visual tasks. However, most approaches primarily operate in the raw pixel space of images, which limits the exploration of the frequency characteristics of underwater images, leading to an inadequate utilization of deep models' representational capabilities in producing high-quality images. In this paper, we introduce a novel Underwater Image Enhancement (UIE) framework, named WF-Diff, designed to fully leverage the characteristics of frequency domain information and diffusi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.16845","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/2311.16845/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":"2311.16845","created_at":"2026-07-05T07:17:47.308941+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.16845v1","created_at":"2026-07-05T07:17:47.308941+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16845","created_at":"2026-07-05T07:17:47.308941+00:00"},{"alias_kind":"pith_short_12","alias_value":"WG2AOBGVI73O","created_at":"2026-07-05T07:17:47.308941+00:00"},{"alias_kind":"pith_short_16","alias_value":"WG2AOBGVI73OZCNS","created_at":"2026-07-05T07:17:47.308941+00:00"},{"alias_kind":"pith_short_8","alias_value":"WG2AOBGV","created_at":"2026-07-05T07:17:47.308941+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.16266","citing_title":"Hero-Mamba: Mamba-based Dual Domain Learning for Underwater Image Enhancement","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WG2AOBGVI73OZCNS7AD6C2GIEO","json":"https://pith.science/pith/WG2AOBGVI73OZCNS7AD6C2GIEO.json","graph_json":"https://pith.science/api/pith-number/WG2AOBGVI73OZCNS7AD6C2GIEO/graph.json","events_json":"https://pith.science/api/pith-number/WG2AOBGVI73OZCNS7AD6C2GIEO/events.json","paper":"https://pith.science/paper/WG2AOBGV"},"agent_actions":{"view_html":"https://pith.science/pith/WG2AOBGVI73OZCNS7AD6C2GIEO","download_json":"https://pith.science/pith/WG2AOBGVI73OZCNS7AD6C2GIEO.json","view_paper":"https://pith.science/paper/WG2AOBGV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.16845&json=true","fetch_graph":"https://pith.science/api/pith-number/WG2AOBGVI73OZCNS7AD6C2GIEO/graph.json","fetch_events":"https://pith.science/api/pith-number/WG2AOBGVI73OZCNS7AD6C2GIEO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WG2AOBGVI73OZCNS7AD6C2GIEO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WG2AOBGVI73OZCNS7AD6C2GIEO/action/storage_attestation","attest_author":"https://pith.science/pith/WG2AOBGVI73OZCNS7AD6C2GIEO/action/author_attestation","sign_citation":"https://pith.science/pith/WG2AOBGVI73OZCNS7AD6C2GIEO/action/citation_signature","submit_replication":"https://pith.science/pith/WG2AOBGVI73OZCNS7AD6C2GIEO/action/replication_record"}},"created_at":"2026-07-05T07:17:47.308941+00:00","updated_at":"2026-07-05T07:17:47.308941+00:00"}