{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2AFSZFB4Z4KITPH2HEXPTSELSO","short_pith_number":"pith:2AFSZFB4","schema_version":"1.0","canonical_sha256":"d00b2c943ccf1489bcfa392ef9c88b938dc10a6ac03ffa0f6ffa9e766a249bfc","source":{"kind":"arxiv","id":"2410.14595","version":2},"attestation_state":"computed","paper":{"title":"DRACO-DehazeNet: An Efficient Image Dehazing Network Combining Detail Recovery and a Novel Contrastive Learning Paradigm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daniel Puiu Poenar, Gao Yu Lee, Md Meftahul Ferdaus, Tanmoy Dam, Vu Duong","submitted_at":"2024-10-18T16:48:31Z","abstract_excerpt":"Image dehazing is crucial for clarifying images obscured by haze or fog, but current learning-based approaches is dependent on large volumes of training data and hence consumed significant computational power. Additionally, their performance is often inadequate under non-uniform or heavy haze. To address these challenges, we developed the Detail Recovery And Contrastive DehazeNet, which facilitates efficient and effective dehazing via a dense dilated inverted residual block and an attention-based detail recovery network that tailors enhancements to specific dehazed scene contexts. A major inno"},"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":"2410.14595","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-18T16:48:31Z","cross_cats_sorted":[],"title_canon_sha256":"7ebb09cdb5c5c9c46daedf0126df954252a68424c0a85b67f97586cc237eb65e","abstract_canon_sha256":"1c2a8cd0423e1debf7607288c248800e4b00f9a0bef7eef251685727d7634fe7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:25:12.128883Z","signature_b64":"oHj+2lSsHI8R8Nzg+D0uC7/A2+b02wR+vslkECIvycJ3ftPhvO35cBpSrdntJXW6JkXtcqnFd3qG5VRV0i9uAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d00b2c943ccf1489bcfa392ef9c88b938dc10a6ac03ffa0f6ffa9e766a249bfc","last_reissued_at":"2026-07-05T10:25:12.128390Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:25:12.128390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DRACO-DehazeNet: An Efficient Image Dehazing Network Combining Detail Recovery and a Novel Contrastive Learning Paradigm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daniel Puiu Poenar, Gao Yu Lee, Md Meftahul Ferdaus, Tanmoy Dam, Vu Duong","submitted_at":"2024-10-18T16:48:31Z","abstract_excerpt":"Image dehazing is crucial for clarifying images obscured by haze or fog, but current learning-based approaches is dependent on large volumes of training data and hence consumed significant computational power. Additionally, their performance is often inadequate under non-uniform or heavy haze. To address these challenges, we developed the Detail Recovery And Contrastive DehazeNet, which facilitates efficient and effective dehazing via a dense dilated inverted residual block and an attention-based detail recovery network that tailors enhancements to specific dehazed scene contexts. A major inno"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.14595","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/2410.14595/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":"2410.14595","created_at":"2026-07-05T10:25:12.128444+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.14595v2","created_at":"2026-07-05T10:25:12.128444+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.14595","created_at":"2026-07-05T10:25:12.128444+00:00"},{"alias_kind":"pith_short_12","alias_value":"2AFSZFB4Z4KI","created_at":"2026-07-05T10:25:12.128444+00:00"},{"alias_kind":"pith_short_16","alias_value":"2AFSZFB4Z4KITPH2","created_at":"2026-07-05T10:25:12.128444+00:00"},{"alias_kind":"pith_short_8","alias_value":"2AFSZFB4","created_at":"2026-07-05T10:25:12.128444+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.18326","citing_title":"Enhancing Few-Shot Classification of Benchmark and Disaster Imagery with ABHFA-Net","ref_index":46,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2AFSZFB4Z4KITPH2HEXPTSELSO","json":"https://pith.science/pith/2AFSZFB4Z4KITPH2HEXPTSELSO.json","graph_json":"https://pith.science/api/pith-number/2AFSZFB4Z4KITPH2HEXPTSELSO/graph.json","events_json":"https://pith.science/api/pith-number/2AFSZFB4Z4KITPH2HEXPTSELSO/events.json","paper":"https://pith.science/paper/2AFSZFB4"},"agent_actions":{"view_html":"https://pith.science/pith/2AFSZFB4Z4KITPH2HEXPTSELSO","download_json":"https://pith.science/pith/2AFSZFB4Z4KITPH2HEXPTSELSO.json","view_paper":"https://pith.science/paper/2AFSZFB4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.14595&json=true","fetch_graph":"https://pith.science/api/pith-number/2AFSZFB4Z4KITPH2HEXPTSELSO/graph.json","fetch_events":"https://pith.science/api/pith-number/2AFSZFB4Z4KITPH2HEXPTSELSO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2AFSZFB4Z4KITPH2HEXPTSELSO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2AFSZFB4Z4KITPH2HEXPTSELSO/action/storage_attestation","attest_author":"https://pith.science/pith/2AFSZFB4Z4KITPH2HEXPTSELSO/action/author_attestation","sign_citation":"https://pith.science/pith/2AFSZFB4Z4KITPH2HEXPTSELSO/action/citation_signature","submit_replication":"https://pith.science/pith/2AFSZFB4Z4KITPH2HEXPTSELSO/action/replication_record"}},"created_at":"2026-07-05T10:25:12.128444+00:00","updated_at":"2026-07-05T10:25:12.128444+00:00"}