{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:E7BEPWB2RDJ3YGQ6DG7YSBIFYS","short_pith_number":"pith:E7BEPWB2","canonical_record":{"source":{"id":"2305.17654","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-28T07:41:10Z","cross_cats_sorted":[],"title_canon_sha256":"78e8875c33064960bae4a3eed524e75ea1f5319478429943dc982127279abbae","abstract_canon_sha256":"83ab1159bee09a1b58d424228541e22b106a3939e7478afb12727a12b2311fc0"},"schema_version":"1.0"},"canonical_sha256":"27c247d83a88d3bc1a1e19bf890505c490672dddb36451ba3beee75e74fe8b44","source":{"kind":"arxiv","id":"2305.17654","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.17654","created_at":"2026-07-05T06:14:46Z"},{"alias_kind":"arxiv_version","alias_value":"2305.17654v1","created_at":"2026-07-05T06:14:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.17654","created_at":"2026-07-05T06:14:46Z"},{"alias_kind":"pith_short_12","alias_value":"E7BEPWB2RDJ3","created_at":"2026-07-05T06:14:46Z"},{"alias_kind":"pith_short_16","alias_value":"E7BEPWB2RDJ3YGQ6","created_at":"2026-07-05T06:14:46Z"},{"alias_kind":"pith_short_8","alias_value":"E7BEPWB2","created_at":"2026-07-05T06:14:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:E7BEPWB2RDJ3YGQ6DG7YSBIFYS","target":"record","payload":{"canonical_record":{"source":{"id":"2305.17654","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-28T07:41:10Z","cross_cats_sorted":[],"title_canon_sha256":"78e8875c33064960bae4a3eed524e75ea1f5319478429943dc982127279abbae","abstract_canon_sha256":"83ab1159bee09a1b58d424228541e22b106a3939e7478afb12727a12b2311fc0"},"schema_version":"1.0"},"canonical_sha256":"27c247d83a88d3bc1a1e19bf890505c490672dddb36451ba3beee75e74fe8b44","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:46.721228Z","signature_b64":"dil705nA7ZMVppY7aywL6v2JwyxbLa4rEYq0NhyD3qvWAdacp5EydJt5rUfRNsFqsfTnfRki2kgNasuRu/HoAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27c247d83a88d3bc1a1e19bf890505c490672dddb36451ba3beee75e74fe8b44","last_reissued_at":"2026-07-05T06:14:46.720860Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:46.720860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.17654","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:14:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AFXps8n6HX1UDvQIsPTlrvha7gYBxtjWk92WxqzP9vWLaPVQVU90j4oV1vfy8/SfjxTpw5fAOUYeZB5urkmEBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T09:10:15.747858Z"},"content_sha256":"eb6a121572a9630ae57545256fefc9d7ee19ae71caade033084a441dad420f46","schema_version":"1.0","event_id":"sha256:eb6a121572a9630ae57545256fefc9d7ee19ae71caade033084a441dad420f46"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:E7BEPWB2RDJ3YGQ6DG7YSBIFYS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MixDehazeNet : Mix Structure Block For Image Dehazing Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingrong Xu, Duanfeng Chu, Liping Lu, Qian Xiong","submitted_at":"2023-05-28T07:41:10Z","abstract_excerpt":"Image dehazing is a typical task in the low-level vision field. Previous studies verified the effectiveness of the large convolutional kernel and attention mechanism in dehazing. However, there are two drawbacks: the multi-scale properties of an image are readily ignored when a large convolutional kernel is introduced, and the standard series connection of an attention module does not sufficiently consider an uneven hazy distribution. In this paper, we propose a novel framework named Mix Structure Image Dehazing Network (MixDehazeNet), which solves two issues mentioned above. Specifically, it "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.17654","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/2305.17654/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:14:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bY5C1jGnxJx2Vt7J3QXKYRi2d2Hd7OQmaLt3WHbtK4HZ5dvkQB5brIt1AU0tufg2hPLlxVmjUTw9F5f4Og7qAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T09:10:15.748511Z"},"content_sha256":"fa02a293a3bc01d7a22116c477c2b9bf8a4c446b3ab3b32dc8c60e1c73e306ba","schema_version":"1.0","event_id":"sha256:fa02a293a3bc01d7a22116c477c2b9bf8a4c446b3ab3b32dc8c60e1c73e306ba"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E7BEPWB2RDJ3YGQ6DG7YSBIFYS/bundle.json","state_url":"https://pith.science/pith/E7BEPWB2RDJ3YGQ6DG7YSBIFYS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E7BEPWB2RDJ3YGQ6DG7YSBIFYS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-16T09:10:15Z","links":{"resolver":"https://pith.science/pith/E7BEPWB2RDJ3YGQ6DG7YSBIFYS","bundle":"https://pith.science/pith/E7BEPWB2RDJ3YGQ6DG7YSBIFYS/bundle.json","state":"https://pith.science/pith/E7BEPWB2RDJ3YGQ6DG7YSBIFYS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E7BEPWB2RDJ3YGQ6DG7YSBIFYS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:E7BEPWB2RDJ3YGQ6DG7YSBIFYS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"83ab1159bee09a1b58d424228541e22b106a3939e7478afb12727a12b2311fc0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-28T07:41:10Z","title_canon_sha256":"78e8875c33064960bae4a3eed524e75ea1f5319478429943dc982127279abbae"},"schema_version":"1.0","source":{"id":"2305.17654","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.17654","created_at":"2026-07-05T06:14:46Z"},{"alias_kind":"arxiv_version","alias_value":"2305.17654v1","created_at":"2026-07-05T06:14:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.17654","created_at":"2026-07-05T06:14:46Z"},{"alias_kind":"pith_short_12","alias_value":"E7BEPWB2RDJ3","created_at":"2026-07-05T06:14:46Z"},{"alias_kind":"pith_short_16","alias_value":"E7BEPWB2RDJ3YGQ6","created_at":"2026-07-05T06:14:46Z"},{"alias_kind":"pith_short_8","alias_value":"E7BEPWB2","created_at":"2026-07-05T06:14:46Z"}],"graph_snapshots":[{"event_id":"sha256:fa02a293a3bc01d7a22116c477c2b9bf8a4c446b3ab3b32dc8c60e1c73e306ba","target":"graph","created_at":"2026-07-05T06:14:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2305.17654/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Image dehazing is a typical task in the low-level vision field. Previous studies verified the effectiveness of the large convolutional kernel and attention mechanism in dehazing. However, there are two drawbacks: the multi-scale properties of an image are readily ignored when a large convolutional kernel is introduced, and the standard series connection of an attention module does not sufficiently consider an uneven hazy distribution. In this paper, we propose a novel framework named Mix Structure Image Dehazing Network (MixDehazeNet), which solves two issues mentioned above. Specifically, it ","authors_text":"Bingrong Xu, Duanfeng Chu, Liping Lu, Qian Xiong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-28T07:41:10Z","title":"MixDehazeNet : Mix Structure Block For Image Dehazing Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.17654","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:eb6a121572a9630ae57545256fefc9d7ee19ae71caade033084a441dad420f46","target":"record","created_at":"2026-07-05T06:14:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"83ab1159bee09a1b58d424228541e22b106a3939e7478afb12727a12b2311fc0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-28T07:41:10Z","title_canon_sha256":"78e8875c33064960bae4a3eed524e75ea1f5319478429943dc982127279abbae"},"schema_version":"1.0","source":{"id":"2305.17654","kind":"arxiv","version":1}},"canonical_sha256":"27c247d83a88d3bc1a1e19bf890505c490672dddb36451ba3beee75e74fe8b44","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"27c247d83a88d3bc1a1e19bf890505c490672dddb36451ba3beee75e74fe8b44","first_computed_at":"2026-07-05T06:14:46.720860Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:14:46.720860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dil705nA7ZMVppY7aywL6v2JwyxbLa4rEYq0NhyD3qvWAdacp5EydJt5rUfRNsFqsfTnfRki2kgNasuRu/HoAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:14:46.721228Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.17654","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb6a121572a9630ae57545256fefc9d7ee19ae71caade033084a441dad420f46","sha256:fa02a293a3bc01d7a22116c477c2b9bf8a4c446b3ab3b32dc8c60e1c73e306ba"],"state_sha256":"08d714cde38a2942e09818d9bfbebf44e92a2d487861bebd3d7e5f3612403998"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0et8CSjWv5Eoc1EDpoHfq7Lop5/2NIC1sccqPY24KKc2KVFHgJ+qZhh8iNolvRj+PhR55nhexFxQK1kwlLlaCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T09:10:15.753464Z","bundle_sha256":"c51089f69441d41d6b2b717801b971ee282928c5b6c5680b405bf0baa6b9306e"}}