{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7FBLTG4LGK472CQ4IIUNLQW36G","short_pith_number":"pith:7FBLTG4L","schema_version":"1.0","canonical_sha256":"f942b99b8b32b9fd0a1c4228d5c2dbf1b2d893143cdc1a010ca42ee9f043d376","source":{"kind":"arxiv","id":"2304.14612","version":1},"attestation_state":"computed","paper":{"title":"Local-Global Transformer Enhanced Unfolding Network for Pan-sharpening","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Gongping Yang, Mingsong Li, Tao Xiao, Yikun Liu, Yuwen Huang","submitted_at":"2023-04-28T03:34:36Z","abstract_excerpt":"Pan-sharpening aims to increase the spatial resolution of the low-resolution multispectral (LrMS) image with the guidance of the corresponding panchromatic (PAN) image. Although deep learning (DL)-based pan-sharpening methods have achieved promising performance, most of them have a two-fold deficiency. For one thing, the universally adopted black box principle limits the model interpretability. For another thing, existing DL-based methods fail to efficiently capture local and global dependencies at the same time, inevitably limiting the overall performance. To address these mentioned issues, w"},"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":"2304.14612","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-28T03:34:36Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"1d4a109cfaf448dbd6b4739c36bcb7c0d77835cace9d10fe3d6880e6bffec5d2","abstract_canon_sha256":"1b38cf1943e9d939438e59d8bbab91350a4c152b9676844b5974ad09d416c644"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:05:19.106529Z","signature_b64":"MKScsJqxZfq5y27IjrOVcFHeTpdL/qncsmGEfLiuJNSO8O2QZEeNEV0CpMs7uki6Y4qo+19M7O41dmR3QAIvCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f942b99b8b32b9fd0a1c4228d5c2dbf1b2d893143cdc1a010ca42ee9f043d376","last_reissued_at":"2026-07-05T06:05:19.106077Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:05:19.106077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Local-Global Transformer Enhanced Unfolding Network for Pan-sharpening","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Gongping Yang, Mingsong Li, Tao Xiao, Yikun Liu, Yuwen Huang","submitted_at":"2023-04-28T03:34:36Z","abstract_excerpt":"Pan-sharpening aims to increase the spatial resolution of the low-resolution multispectral (LrMS) image with the guidance of the corresponding panchromatic (PAN) image. Although deep learning (DL)-based pan-sharpening methods have achieved promising performance, most of them have a two-fold deficiency. For one thing, the universally adopted black box principle limits the model interpretability. For another thing, existing DL-based methods fail to efficiently capture local and global dependencies at the same time, inevitably limiting the overall performance. To address these mentioned issues, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.14612","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/2304.14612/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":"2304.14612","created_at":"2026-07-05T06:05:19.106141+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.14612v1","created_at":"2026-07-05T06:05:19.106141+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.14612","created_at":"2026-07-05T06:05:19.106141+00:00"},{"alias_kind":"pith_short_12","alias_value":"7FBLTG4LGK47","created_at":"2026-07-05T06:05:19.106141+00:00"},{"alias_kind":"pith_short_16","alias_value":"7FBLTG4LGK472CQ4","created_at":"2026-07-05T06:05:19.106141+00:00"},{"alias_kind":"pith_short_8","alias_value":"7FBLTG4L","created_at":"2026-07-05T06:05:19.106141+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.10461","citing_title":"RAPNet: A Receptive-Field Adaptive Convolutional Neural Network for Pansharpening","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7FBLTG4LGK472CQ4IIUNLQW36G","json":"https://pith.science/pith/7FBLTG4LGK472CQ4IIUNLQW36G.json","graph_json":"https://pith.science/api/pith-number/7FBLTG4LGK472CQ4IIUNLQW36G/graph.json","events_json":"https://pith.science/api/pith-number/7FBLTG4LGK472CQ4IIUNLQW36G/events.json","paper":"https://pith.science/paper/7FBLTG4L"},"agent_actions":{"view_html":"https://pith.science/pith/7FBLTG4LGK472CQ4IIUNLQW36G","download_json":"https://pith.science/pith/7FBLTG4LGK472CQ4IIUNLQW36G.json","view_paper":"https://pith.science/paper/7FBLTG4L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.14612&json=true","fetch_graph":"https://pith.science/api/pith-number/7FBLTG4LGK472CQ4IIUNLQW36G/graph.json","fetch_events":"https://pith.science/api/pith-number/7FBLTG4LGK472CQ4IIUNLQW36G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7FBLTG4LGK472CQ4IIUNLQW36G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7FBLTG4LGK472CQ4IIUNLQW36G/action/storage_attestation","attest_author":"https://pith.science/pith/7FBLTG4LGK472CQ4IIUNLQW36G/action/author_attestation","sign_citation":"https://pith.science/pith/7FBLTG4LGK472CQ4IIUNLQW36G/action/citation_signature","submit_replication":"https://pith.science/pith/7FBLTG4LGK472CQ4IIUNLQW36G/action/replication_record"}},"created_at":"2026-07-05T06:05:19.106141+00:00","updated_at":"2026-07-05T06:05:19.106141+00:00"}