{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3OZJE7WDKZOXEXOWJIIDOGTVIG","short_pith_number":"pith:3OZJE7WD","schema_version":"1.0","canonical_sha256":"dbb2927ec3565d725dd64a10371a75419df84bd37632b27a3fbd35bc1c532548","source":{"kind":"arxiv","id":"2304.08444","version":1},"attestation_state":"computed","paper":{"title":"SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingxiang Qu, Ryan Wen Liu, Shengfeng He, Wenqi Ren, Yuan Gao, Yu Guo, Yuxu Lu","submitted_at":"2023-04-17T17:05:29Z","abstract_excerpt":"The presence of non-homogeneous haze can cause scene blurring, color distortion, low contrast, and other degradations that obscure texture details. Existing homogeneous dehazing methods struggle to handle the non-uniform distribution of haze in a robust manner. The crucial challenge of non-homogeneous dehazing is to effectively extract the non-uniform distribution features and reconstruct the details of hazy areas with high quality. In this paper, we propose a novel self-paced semi-curricular attention network, called SCANet, for non-homogeneous image dehazing that focuses on enhancing haze-oc"},"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":"2304.08444","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-17T17:05:29Z","cross_cats_sorted":[],"title_canon_sha256":"9ff609b4fb48edc1db50b9179eae5e10e4981efd22196ce94155fbb068d3648e","abstract_canon_sha256":"46c0b8555e4c662570d649de3578f9d437c0f2f2f1b3c9ad444c6d648937331d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:01:52.571874Z","signature_b64":"3Pwj9Dx5nmlfXZ6sjIT1fRLXwiwyA+Cq641VawkkitQyCT3y/Fv3Icko9EtsstV319fSU4J8g6QPrpqagqEOCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dbb2927ec3565d725dd64a10371a75419df84bd37632b27a3fbd35bc1c532548","last_reissued_at":"2026-07-05T06:01:52.571499Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:01:52.571499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingxiang Qu, Ryan Wen Liu, Shengfeng He, Wenqi Ren, Yuan Gao, Yu Guo, Yuxu Lu","submitted_at":"2023-04-17T17:05:29Z","abstract_excerpt":"The presence of non-homogeneous haze can cause scene blurring, color distortion, low contrast, and other degradations that obscure texture details. Existing homogeneous dehazing methods struggle to handle the non-uniform distribution of haze in a robust manner. The crucial challenge of non-homogeneous dehazing is to effectively extract the non-uniform distribution features and reconstruct the details of hazy areas with high quality. In this paper, we propose a novel self-paced semi-curricular attention network, called SCANet, for non-homogeneous image dehazing that focuses on enhancing haze-oc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.08444","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/2304.08444/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":"2304.08444","created_at":"2026-07-05T06:01:52.571554+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.08444v1","created_at":"2026-07-05T06:01:52.571554+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.08444","created_at":"2026-07-05T06:01:52.571554+00:00"},{"alias_kind":"pith_short_12","alias_value":"3OZJE7WDKZOX","created_at":"2026-07-05T06:01:52.571554+00:00"},{"alias_kind":"pith_short_16","alias_value":"3OZJE7WDKZOXEXOW","created_at":"2026-07-05T06:01:52.571554+00:00"},{"alias_kind":"pith_short_8","alias_value":"3OZJE7WD","created_at":"2026-07-05T06:01:52.571554+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/3OZJE7WDKZOXEXOWJIIDOGTVIG","json":"https://pith.science/pith/3OZJE7WDKZOXEXOWJIIDOGTVIG.json","graph_json":"https://pith.science/api/pith-number/3OZJE7WDKZOXEXOWJIIDOGTVIG/graph.json","events_json":"https://pith.science/api/pith-number/3OZJE7WDKZOXEXOWJIIDOGTVIG/events.json","paper":"https://pith.science/paper/3OZJE7WD"},"agent_actions":{"view_html":"https://pith.science/pith/3OZJE7WDKZOXEXOWJIIDOGTVIG","download_json":"https://pith.science/pith/3OZJE7WDKZOXEXOWJIIDOGTVIG.json","view_paper":"https://pith.science/paper/3OZJE7WD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.08444&json=true","fetch_graph":"https://pith.science/api/pith-number/3OZJE7WDKZOXEXOWJIIDOGTVIG/graph.json","fetch_events":"https://pith.science/api/pith-number/3OZJE7WDKZOXEXOWJIIDOGTVIG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3OZJE7WDKZOXEXOWJIIDOGTVIG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3OZJE7WDKZOXEXOWJIIDOGTVIG/action/storage_attestation","attest_author":"https://pith.science/pith/3OZJE7WDKZOXEXOWJIIDOGTVIG/action/author_attestation","sign_citation":"https://pith.science/pith/3OZJE7WDKZOXEXOWJIIDOGTVIG/action/citation_signature","submit_replication":"https://pith.science/pith/3OZJE7WDKZOXEXOWJIIDOGTVIG/action/replication_record"}},"created_at":"2026-07-05T06:01:52.571554+00:00","updated_at":"2026-07-05T06:01:52.571554+00:00"}