{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:S457JYO4SWJSCCZFRBRN5GXEQ4","short_pith_number":"pith:S457JYO4","schema_version":"1.0","canonical_sha256":"973bf4e1dc9593210b258862de9ae4873e2ba69526a5544ca4c6c807dac04a39","source":{"kind":"arxiv","id":"2501.01460","version":4},"attestation_state":"computed","paper":{"title":"GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Guojing Zhang, Jianqiang Huang, Kai Li, Qiwei Zhu, Xiaoying Wang, Xilai Li","submitted_at":"2024-12-31T10:43:19Z","abstract_excerpt":"In recent years, deep neural networks, including Convolutional Neural Networks, Transformers, and State Space Models, have achieved significant progress in Remote Sensing Image (RSI) Super-Resolution (SR). However, existing SR methods typically overlook the complementary relationship between global and local dependencies. These methods either focus on capturing local information or prioritize global information, which results in models that are unable to effectively capture both global and local features simultaneously. Moreover, their computational cost becomes prohibitive when applied to lar"},"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":"2501.01460","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-12-31T10:43:19Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"d8987d40a103496644ee3bd049d3dd60bc950b92dcaacac2a4ddf7a289e29173","abstract_canon_sha256":"4af8f369dba1362b8f9d5b71ad9f90f4f1b28aa0d4030a67c73e13bc3d19c9a6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:54:29.100288Z","signature_b64":"5MDnurTwSM0J4A0hlT85/GUmuJMza6cGD05Cf0isVWaPrTppQf9FZcSfXp8fhIHSKBrJGnxhOcSHQQxKzy9VBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"973bf4e1dc9593210b258862de9ae4873e2ba69526a5544ca4c6c807dac04a39","last_reissued_at":"2026-07-05T11:54:29.099798Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:54:29.099798Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Guojing Zhang, Jianqiang Huang, Kai Li, Qiwei Zhu, Xiaoying Wang, Xilai Li","submitted_at":"2024-12-31T10:43:19Z","abstract_excerpt":"In recent years, deep neural networks, including Convolutional Neural Networks, Transformers, and State Space Models, have achieved significant progress in Remote Sensing Image (RSI) Super-Resolution (SR). However, existing SR methods typically overlook the complementary relationship between global and local dependencies. These methods either focus on capturing local information or prioritize global information, which results in models that are unable to effectively capture both global and local features simultaneously. Moreover, their computational cost becomes prohibitive when applied to lar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.01460","kind":"arxiv","version":4},"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/2501.01460/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":"2501.01460","created_at":"2026-07-05T11:54:29.099854+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.01460v4","created_at":"2026-07-05T11:54:29.099854+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.01460","created_at":"2026-07-05T11:54:29.099854+00:00"},{"alias_kind":"pith_short_12","alias_value":"S457JYO4SWJS","created_at":"2026-07-05T11:54:29.099854+00:00"},{"alias_kind":"pith_short_16","alias_value":"S457JYO4SWJSCCZF","created_at":"2026-07-05T11:54:29.099854+00:00"},{"alias_kind":"pith_short_8","alias_value":"S457JYO4","created_at":"2026-07-05T11:54:29.099854+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/S457JYO4SWJSCCZFRBRN5GXEQ4","json":"https://pith.science/pith/S457JYO4SWJSCCZFRBRN5GXEQ4.json","graph_json":"https://pith.science/api/pith-number/S457JYO4SWJSCCZFRBRN5GXEQ4/graph.json","events_json":"https://pith.science/api/pith-number/S457JYO4SWJSCCZFRBRN5GXEQ4/events.json","paper":"https://pith.science/paper/S457JYO4"},"agent_actions":{"view_html":"https://pith.science/pith/S457JYO4SWJSCCZFRBRN5GXEQ4","download_json":"https://pith.science/pith/S457JYO4SWJSCCZFRBRN5GXEQ4.json","view_paper":"https://pith.science/paper/S457JYO4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.01460&json=true","fetch_graph":"https://pith.science/api/pith-number/S457JYO4SWJSCCZFRBRN5GXEQ4/graph.json","fetch_events":"https://pith.science/api/pith-number/S457JYO4SWJSCCZFRBRN5GXEQ4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S457JYO4SWJSCCZFRBRN5GXEQ4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S457JYO4SWJSCCZFRBRN5GXEQ4/action/storage_attestation","attest_author":"https://pith.science/pith/S457JYO4SWJSCCZFRBRN5GXEQ4/action/author_attestation","sign_citation":"https://pith.science/pith/S457JYO4SWJSCCZFRBRN5GXEQ4/action/citation_signature","submit_replication":"https://pith.science/pith/S457JYO4SWJSCCZFRBRN5GXEQ4/action/replication_record"}},"created_at":"2026-07-05T11:54:29.099854+00:00","updated_at":"2026-07-05T11:54:29.099854+00:00"}