{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:MNWPHADAE5E2RNBRSTN4NPZSFM","short_pith_number":"pith:MNWPHADA","canonical_record":{"source":{"id":"1803.07955","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-21T15:01:21Z","cross_cats_sorted":[],"title_canon_sha256":"69840d353a38815b57eb0b91293334733eaacb91568e80f938659c69426c80bd","abstract_canon_sha256":"366662c51a8c780061da62ccf674edd0a33a40708489a37fd92d93473e854d98"},"schema_version":"1.0"},"canonical_sha256":"636cf380602749a8b43194dbc6bf322b0a90a37f2a712d6bfaa530a64ae78d32","source":{"kind":"arxiv","id":"1803.07955","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.07955","created_at":"2026-05-18T00:20:28Z"},{"alias_kind":"arxiv_version","alias_value":"1803.07955v1","created_at":"2026-05-18T00:20:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.07955","created_at":"2026-05-18T00:20:28Z"},{"alias_kind":"pith_short_12","alias_value":"MNWPHADAE5E2","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"MNWPHADAE5E2RNBR","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"MNWPHADA","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:MNWPHADAE5E2RNBRSTN4NPZSFM","target":"record","payload":{"canonical_record":{"source":{"id":"1803.07955","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-21T15:01:21Z","cross_cats_sorted":[],"title_canon_sha256":"69840d353a38815b57eb0b91293334733eaacb91568e80f938659c69426c80bd","abstract_canon_sha256":"366662c51a8c780061da62ccf674edd0a33a40708489a37fd92d93473e854d98"},"schema_version":"1.0"},"canonical_sha256":"636cf380602749a8b43194dbc6bf322b0a90a37f2a712d6bfaa530a64ae78d32","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:28.414768Z","signature_b64":"IkV6YrvVe12BN2m0keTKU2bsGWYMHqD+Zs9A90e/vesx5bp1yhQpQFtIVJovzOHFqGGB3o6eoL7avLVni7opCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"636cf380602749a8b43194dbc6bf322b0a90a37f2a712d6bfaa530a64ae78d32","last_reissued_at":"2026-05-18T00:20:28.414285Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:28.414285Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.07955","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":"2026-05-18T00:20:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RtqCgpHUHjpzRsFXFQ19EwLtR0xReev8tsHlA9Elwo+JPDpDJ6TQV4i5cXzWTeEknW1vNbLYXsvWjA85NjUNDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T01:55:11.541120Z"},"content_sha256":"4e3c6a0b96ff682f653ae9a4f14ab2d70c2c5f31478a3d56657403c0fe7eaa35","schema_version":"1.0","event_id":"sha256:4e3c6a0b96ff682f653ae9a4f14ab2d70c2c5f31478a3d56657403c0fe7eaa35"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:MNWPHADAE5E2RNBRSTN4NPZSFM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Cascaded Convolutional Neural Network for Single Image Dehazing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chongyi Li, Fatih Porikli, Huazhu Fu, Jichang Guo, Yanwei Pang","submitted_at":"2018-03-21T15:01:21Z","abstract_excerpt":"Images captured under outdoor scenes usually suffer from low contrast and limited visibility due to suspended atmospheric particles, which directly affects the quality of photos. Despite numerous image dehazing methods have been proposed, effective hazy image restoration remains a challenging problem. Existing learning-based methods usually predict the medium transmission by Convolutional Neural Networks (CNNs), but ignore the key global atmospheric light. Different from previous learning-based methods, we propose a flexible cascaded CNN for single hazy image restoration, which considers the m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.07955","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":""},"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":"2026-05-18T00:20:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MIP21LpC0rCOHTGVbTbMq7Ys8uA0gosA4H8WVNslD3B5vQK3qZDgg0lcGYf+yafemU9Bo1xlQPE71yQkTciRDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T01:55:11.541787Z"},"content_sha256":"33fa7bcb30a127db8c879af81e44c9d7c76a3e37ba4f5653fa6044e97a4912d6","schema_version":"1.0","event_id":"sha256:33fa7bcb30a127db8c879af81e44c9d7c76a3e37ba4f5653fa6044e97a4912d6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MNWPHADAE5E2RNBRSTN4NPZSFM/bundle.json","state_url":"https://pith.science/pith/MNWPHADAE5E2RNBRSTN4NPZSFM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MNWPHADAE5E2RNBRSTN4NPZSFM/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":"2026-06-04T01:55:11Z","links":{"resolver":"https://pith.science/pith/MNWPHADAE5E2RNBRSTN4NPZSFM","bundle":"https://pith.science/pith/MNWPHADAE5E2RNBRSTN4NPZSFM/bundle.json","state":"https://pith.science/pith/MNWPHADAE5E2RNBRSTN4NPZSFM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MNWPHADAE5E2RNBRSTN4NPZSFM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:MNWPHADAE5E2RNBRSTN4NPZSFM","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":"366662c51a8c780061da62ccf674edd0a33a40708489a37fd92d93473e854d98","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-21T15:01:21Z","title_canon_sha256":"69840d353a38815b57eb0b91293334733eaacb91568e80f938659c69426c80bd"},"schema_version":"1.0","source":{"id":"1803.07955","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.07955","created_at":"2026-05-18T00:20:28Z"},{"alias_kind":"arxiv_version","alias_value":"1803.07955v1","created_at":"2026-05-18T00:20:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.07955","created_at":"2026-05-18T00:20:28Z"},{"alias_kind":"pith_short_12","alias_value":"MNWPHADAE5E2","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"MNWPHADAE5E2RNBR","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"MNWPHADA","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:33fa7bcb30a127db8c879af81e44c9d7c76a3e37ba4f5653fa6044e97a4912d6","target":"graph","created_at":"2026-05-18T00:20:28Z","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"},"paper":{"abstract_excerpt":"Images captured under outdoor scenes usually suffer from low contrast and limited visibility due to suspended atmospheric particles, which directly affects the quality of photos. Despite numerous image dehazing methods have been proposed, effective hazy image restoration remains a challenging problem. Existing learning-based methods usually predict the medium transmission by Convolutional Neural Networks (CNNs), but ignore the key global atmospheric light. Different from previous learning-based methods, we propose a flexible cascaded CNN for single hazy image restoration, which considers the m","authors_text":"Chongyi Li, Fatih Porikli, Huazhu Fu, Jichang Guo, Yanwei Pang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-21T15:01:21Z","title":"A Cascaded Convolutional Neural Network for Single Image Dehazing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.07955","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":"sha256:4e3c6a0b96ff682f653ae9a4f14ab2d70c2c5f31478a3d56657403c0fe7eaa35","target":"record","created_at":"2026-05-18T00:20:28Z","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":"366662c51a8c780061da62ccf674edd0a33a40708489a37fd92d93473e854d98","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-21T15:01:21Z","title_canon_sha256":"69840d353a38815b57eb0b91293334733eaacb91568e80f938659c69426c80bd"},"schema_version":"1.0","source":{"id":"1803.07955","kind":"arxiv","version":1}},"canonical_sha256":"636cf380602749a8b43194dbc6bf322b0a90a37f2a712d6bfaa530a64ae78d32","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"636cf380602749a8b43194dbc6bf322b0a90a37f2a712d6bfaa530a64ae78d32","first_computed_at":"2026-05-18T00:20:28.414285Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:20:28.414285Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IkV6YrvVe12BN2m0keTKU2bsGWYMHqD+Zs9A90e/vesx5bp1yhQpQFtIVJovzOHFqGGB3o6eoL7avLVni7opCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:20:28.414768Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.07955","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4e3c6a0b96ff682f653ae9a4f14ab2d70c2c5f31478a3d56657403c0fe7eaa35","sha256:33fa7bcb30a127db8c879af81e44c9d7c76a3e37ba4f5653fa6044e97a4912d6"],"state_sha256":"0cfd7737a073107424b5407df51551239ac225d7fc7f3b38d81e19a47cb029cc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E00grnpIeTb3VA0IxSZdPbVu12VpFYlj2KFownjsynBlbp52I9IcW5hqKGnsNrymnHdWGJVMUctS+Pi6wCTxDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T01:55:11.545419Z","bundle_sha256":"cf179ded2eca872c2bcb654402b3506d0848ba85369fb8838976f5c090a24223"}}