{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XQS7PJU6NTH65KQLZZ6JOYC6CC","short_pith_number":"pith:XQS7PJU6","schema_version":"1.0","canonical_sha256":"bc25f7a69e6ccfeeaa0bce7c97605e1082441fcfeb54120a18bc4418e9a70880","source":{"kind":"arxiv","id":"2408.12317","version":3},"attestation_state":"computed","paper":{"title":"Towards Optimal Aggregation of Varying Range Dependencies in Haze Removal","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fengying Xie, Haidong Ding, Linpeng Pan, Xiaozhe Zhang, Zhenwei Shi","submitted_at":"2024-08-22T11:51:50Z","abstract_excerpt":"Haze removal aims to restore a clear image from a hazy input. Existing methods achieve notable success by specializing in either short-range dependencies to preserve local details or long-range dependencies to capture global context. Given the complementary strengths of both, a natural progression is to explicitly integrate them within a unified framework and enable their reasonable aggregation. However, this integration remains underexplored. In this paper, we propose DehazeMatic, which simultaneously and explicitly captures both short- and long-range dependencies through a dual-stream design"},"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":"2408.12317","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-08-22T11:51:50Z","cross_cats_sorted":[],"title_canon_sha256":"a60c7f1f64b5f8f1cfb732996d82c00de6702cca0481f95f697e17913f2d880d","abstract_canon_sha256":"7bec69e855da0bc40b3f8d9219b4006e16e1b757f905942d603f88807561b452"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:20.756327Z","signature_b64":"eBqV66BXejBueHwlnlZ1jLbXwsHDsbRzBa5lCCWj6RYNsjw22XBeg/Bj5gFvtd6x6ZMol+a5mA/xIfWO0SGpBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc25f7a69e6ccfeeaa0bce7c97605e1082441fcfeb54120a18bc4418e9a70880","last_reissued_at":"2026-07-05T11:48:20.755764Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:20.755764Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Optimal Aggregation of Varying Range Dependencies in Haze Removal","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fengying Xie, Haidong Ding, Linpeng Pan, Xiaozhe Zhang, Zhenwei Shi","submitted_at":"2024-08-22T11:51:50Z","abstract_excerpt":"Haze removal aims to restore a clear image from a hazy input. Existing methods achieve notable success by specializing in either short-range dependencies to preserve local details or long-range dependencies to capture global context. Given the complementary strengths of both, a natural progression is to explicitly integrate them within a unified framework and enable their reasonable aggregation. However, this integration remains underexplored. In this paper, we propose DehazeMatic, which simultaneously and explicitly captures both short- and long-range dependencies through a dual-stream design"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.12317","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/2408.12317/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":"2408.12317","created_at":"2026-07-05T11:48:20.755830+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.12317v3","created_at":"2026-07-05T11:48:20.755830+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.12317","created_at":"2026-07-05T11:48:20.755830+00:00"},{"alias_kind":"pith_short_12","alias_value":"XQS7PJU6NTH6","created_at":"2026-07-05T11:48:20.755830+00:00"},{"alias_kind":"pith_short_16","alias_value":"XQS7PJU6NTH65KQL","created_at":"2026-07-05T11:48:20.755830+00:00"},{"alias_kind":"pith_short_8","alias_value":"XQS7PJU6","created_at":"2026-07-05T11:48:20.755830+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.10689","citing_title":"CWNet: Causal Wavelet Network for Low-Light Image Enhancement","ref_index":55,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XQS7PJU6NTH65KQLZZ6JOYC6CC","json":"https://pith.science/pith/XQS7PJU6NTH65KQLZZ6JOYC6CC.json","graph_json":"https://pith.science/api/pith-number/XQS7PJU6NTH65KQLZZ6JOYC6CC/graph.json","events_json":"https://pith.science/api/pith-number/XQS7PJU6NTH65KQLZZ6JOYC6CC/events.json","paper":"https://pith.science/paper/XQS7PJU6"},"agent_actions":{"view_html":"https://pith.science/pith/XQS7PJU6NTH65KQLZZ6JOYC6CC","download_json":"https://pith.science/pith/XQS7PJU6NTH65KQLZZ6JOYC6CC.json","view_paper":"https://pith.science/paper/XQS7PJU6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.12317&json=true","fetch_graph":"https://pith.science/api/pith-number/XQS7PJU6NTH65KQLZZ6JOYC6CC/graph.json","fetch_events":"https://pith.science/api/pith-number/XQS7PJU6NTH65KQLZZ6JOYC6CC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XQS7PJU6NTH65KQLZZ6JOYC6CC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XQS7PJU6NTH65KQLZZ6JOYC6CC/action/storage_attestation","attest_author":"https://pith.science/pith/XQS7PJU6NTH65KQLZZ6JOYC6CC/action/author_attestation","sign_citation":"https://pith.science/pith/XQS7PJU6NTH65KQLZZ6JOYC6CC/action/citation_signature","submit_replication":"https://pith.science/pith/XQS7PJU6NTH65KQLZZ6JOYC6CC/action/replication_record"}},"created_at":"2026-07-05T11:48:20.755830+00:00","updated_at":"2026-07-05T11:48:20.755830+00:00"}