{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OFQNJMXTP2MKEBWC2ZPMWQRYO4","short_pith_number":"pith:OFQNJMXT","schema_version":"1.0","canonical_sha256":"7160d4b2f37e98a206c2d65ecb423877162560df007282feca49d40ba932b5ba","source":{"kind":"arxiv","id":"2604.07048","version":2},"attestation_state":"computed","paper":{"title":"PRISM: Rethinking Atmospheric Scattering Reconstruction as a Unified Understanding and Restoration Model for Real-world Dehazing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"PRISM jointly reconstructs clear scenes and scattering variables to improve real-world image dehazing.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengyu Fang, Chenyang Zhu, Chubin Chen, Chunming He, Hongqiu Wang, Longxiang Tang, Sina Farsiu, Xiu Li, Yuelin Zhang","submitted_at":"2026-04-08T13:01:30Z","abstract_excerpt":"Real-world image dehazing (RID) aims to remove haze-induced degradation from real scenes. This task remains challenging due to non-uniform haze distribution, spatially varying color shifts, and the scarcity of paired real hazy-clean data. In PRISM, we propose Proximal Scattering Atmosphere Reconstruction (PSAR), a physically structured framework that jointly reconstructs the clear scene and scattering variables under the atmospheric scattering model, making the restoration process more interpretable in complex real-world conditions. To bridge the synthetic-to-real gap, we design an online non-"},"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":true},"canonical_record":{"source":{"id":"2604.07048","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-08T13:01:30Z","cross_cats_sorted":[],"title_canon_sha256":"fe61213573314abc47bedc85ae04d8d7cf57039b4ec662878c8b637ed19efd0c","abstract_canon_sha256":"35e28fc24de870d926c10a9b6fc82791e43760013af50e6c115f925b12878c7c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:12.676259Z","signature_b64":"o+WW+5fl48pKUCBJ/v1qoqYRQjaxJb3EWL6/it4nBz3/3l7i/QlbJrQJVwuLnxUG0qLowpaWCi9DMXUGeE3sCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7160d4b2f37e98a206c2d65ecb423877162560df007282feca49d40ba932b5ba","last_reissued_at":"2026-06-03T01:05:12.675811Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:12.675811Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PRISM: Rethinking Atmospheric Scattering Reconstruction as a Unified Understanding and Restoration Model for Real-world Dehazing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"PRISM jointly reconstructs clear scenes and scattering variables to improve real-world image dehazing.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengyu Fang, Chenyang Zhu, Chubin Chen, Chunming He, Hongqiu Wang, Longxiang Tang, Sina Farsiu, Xiu Li, Yuelin Zhang","submitted_at":"2026-04-08T13:01:30Z","abstract_excerpt":"Real-world image dehazing (RID) aims to remove haze-induced degradation from real scenes. This task remains challenging due to non-uniform haze distribution, spatially varying color shifts, and the scarcity of paired real hazy-clean data. In PRISM, we propose Proximal Scattering Atmosphere Reconstruction (PSAR), a physically structured framework that jointly reconstructs the clear scene and scattering variables under the atmospheric scattering model, making the restoration process more interpretable in complex real-world conditions. To bridge the synthetic-to-real gap, we design an online non-"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"PRISM achieves state-of-the-art performance on RID tasks through Proximal Scattered Atmosphere Reconstruction (PSAR), a physically structured framework that jointly reconstructs the clear scene and scattering variables under the atmospheric scattering model, combined with an online non-uniform haze synthesis pipeline and Selective Self-distillation Adaptation scheme.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the model's intrinsic scattering understanding can reliably audit residual haze and guide self-refinement in unpaired real-world scenarios without introducing new artifacts or overfitting to synthetic patterns.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"PRISM proposes a physically structured PSAR framework with non-uniform haze synthesis and selective self-distillation adaptation to achieve state-of-the-art real-world image dehazing.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"PRISM jointly reconstructs clear scenes and scattering variables to improve real-world image dehazing.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"9d6dfb958299cc02c5754d59221754e6d5a0b7c7ea259b78f87e83aba3f083f7"},"source":{"id":"2604.07048","kind":"arxiv","version":2},"verdict":{"id":"da5627d1-447d-42d8-841a-32e3924f44c9","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T18:46:11.844145Z","strongest_claim":"PRISM achieves state-of-the-art performance on RID tasks through Proximal Scattered Atmosphere Reconstruction (PSAR), a physically structured framework that jointly reconstructs the clear scene and scattering variables under the atmospheric scattering model, combined with an online non-uniform haze synthesis pipeline and Selective Self-distillation Adaptation scheme.","one_line_summary":"PRISM proposes a physically structured PSAR framework with non-uniform haze synthesis and selective self-distillation adaptation to achieve state-of-the-art real-world image dehazing.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the model's intrinsic scattering understanding can reliably audit residual haze and guide self-refinement in unpaired real-world scenarios without introducing new artifacts or overfitting to synthetic patterns.","pith_extraction_headline":"PRISM jointly reconstructs clear scenes and scattering variables to improve real-world image dehazing."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.07048/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":2,"snapshot_sha256":"7125c7506fa63774af84664caded786c15e56e49a515f1c3f0d00d7d4ad87ff2"},"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":"2604.07048","created_at":"2026-06-03T01:05:12.675862+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.07048v2","created_at":"2026-06-03T01:05:12.675862+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.07048","created_at":"2026-06-03T01:05:12.675862+00:00"},{"alias_kind":"pith_short_12","alias_value":"OFQNJMXTP2MK","created_at":"2026-06-03T01:05:12.675862+00:00"},{"alias_kind":"pith_short_16","alias_value":"OFQNJMXTP2MKEBWC","created_at":"2026-06-03T01:05:12.675862+00:00"},{"alias_kind":"pith_short_8","alias_value":"OFQNJMXT","created_at":"2026-06-03T01:05:12.675862+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.15803","citing_title":"Embedding-perturbed Exploration Preference Optimization for Flow Models","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OFQNJMXTP2MKEBWC2ZPMWQRYO4","json":"https://pith.science/pith/OFQNJMXTP2MKEBWC2ZPMWQRYO4.json","graph_json":"https://pith.science/api/pith-number/OFQNJMXTP2MKEBWC2ZPMWQRYO4/graph.json","events_json":"https://pith.science/api/pith-number/OFQNJMXTP2MKEBWC2ZPMWQRYO4/events.json","paper":"https://pith.science/paper/OFQNJMXT"},"agent_actions":{"view_html":"https://pith.science/pith/OFQNJMXTP2MKEBWC2ZPMWQRYO4","download_json":"https://pith.science/pith/OFQNJMXTP2MKEBWC2ZPMWQRYO4.json","view_paper":"https://pith.science/paper/OFQNJMXT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.07048&json=true","fetch_graph":"https://pith.science/api/pith-number/OFQNJMXTP2MKEBWC2ZPMWQRYO4/graph.json","fetch_events":"https://pith.science/api/pith-number/OFQNJMXTP2MKEBWC2ZPMWQRYO4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OFQNJMXTP2MKEBWC2ZPMWQRYO4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OFQNJMXTP2MKEBWC2ZPMWQRYO4/action/storage_attestation","attest_author":"https://pith.science/pith/OFQNJMXTP2MKEBWC2ZPMWQRYO4/action/author_attestation","sign_citation":"https://pith.science/pith/OFQNJMXTP2MKEBWC2ZPMWQRYO4/action/citation_signature","submit_replication":"https://pith.science/pith/OFQNJMXTP2MKEBWC2ZPMWQRYO4/action/replication_record"}},"created_at":"2026-06-03T01:05:12.675862+00:00","updated_at":"2026-06-03T01:05:12.675862+00:00"}