{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:T6X2JUJQ4UUNAATJB7NN3MUWMJ","short_pith_number":"pith:T6X2JUJQ","schema_version":"1.0","canonical_sha256":"9fafa4d130e528d002690fdaddb2966250455d326a2cccaa1f1c7c29d384a9e3","source":{"kind":"arxiv","id":"2505.10589","version":4},"attestation_state":"computed","paper":{"title":"Super-Resolution Generative Adversarial Networks based Video Enhancement","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.IV"],"primary_cat":"cs.CV","authors_text":"Hacer Ak\\c{c}a, Ka\\u{g}an \\c{C}et\\.in, \\\"Omer Nezih Gerek","submitted_at":"2025-05-14T20:16:51Z","abstract_excerpt":"This study introduces an enhanced approach to video super-resolution by extending ordinary Single-Image Super-Resolution (SISR) Super-Resolution Generative Adversarial Network (SRGAN) structure to handle spatio-temporal data. While SRGAN has proven effective for single-image enhancement, its design does not account for the temporal continuity required in video processing. To address this, a modified framework that incorporates 3D Non-Local Blocks is proposed, which is enabling the model to capture relationships across both spatial and temporal dimensions. An experimental training pipeline is d"},"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":"2505.10589","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-14T20:16:51Z","cross_cats_sorted":["cs.AI","eess.IV"],"title_canon_sha256":"3c5c7b210e35c4ba7e4f2cfc16c1f0e96b897899ffff4c7e58485bf82a714534","abstract_canon_sha256":"f84f3a8b86e5b93189c3c902950f8f1d61e2e181a59440235aad0ebbc8b3817a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:11.443332Z","signature_b64":"D/eBodosCBORsNZvLLiQ9xFmmz/B0S8h5pt3fsKCy8HpOuw4ExTwSEKAagM9UITM5RZpmRaAOARdkrg/TNJ9Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9fafa4d130e528d002690fdaddb2966250455d326a2cccaa1f1c7c29d384a9e3","last_reissued_at":"2026-07-05T11:29:11.442797Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:11.442797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Super-Resolution Generative Adversarial Networks based Video Enhancement","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.IV"],"primary_cat":"cs.CV","authors_text":"Hacer Ak\\c{c}a, Ka\\u{g}an \\c{C}et\\.in, \\\"Omer Nezih Gerek","submitted_at":"2025-05-14T20:16:51Z","abstract_excerpt":"This study introduces an enhanced approach to video super-resolution by extending ordinary Single-Image Super-Resolution (SISR) Super-Resolution Generative Adversarial Network (SRGAN) structure to handle spatio-temporal data. While SRGAN has proven effective for single-image enhancement, its design does not account for the temporal continuity required in video processing. To address this, a modified framework that incorporates 3D Non-Local Blocks is proposed, which is enabling the model to capture relationships across both spatial and temporal dimensions. An experimental training pipeline is d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10589","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/2505.10589/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":"2505.10589","created_at":"2026-07-05T11:29:11.442864+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.10589v4","created_at":"2026-07-05T11:29:11.442864+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10589","created_at":"2026-07-05T11:29:11.442864+00:00"},{"alias_kind":"pith_short_12","alias_value":"T6X2JUJQ4UUN","created_at":"2026-07-05T11:29:11.442864+00:00"},{"alias_kind":"pith_short_16","alias_value":"T6X2JUJQ4UUNAATJ","created_at":"2026-07-05T11:29:11.442864+00:00"},{"alias_kind":"pith_short_8","alias_value":"T6X2JUJQ","created_at":"2026-07-05T11:29:11.442864+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/T6X2JUJQ4UUNAATJB7NN3MUWMJ","json":"https://pith.science/pith/T6X2JUJQ4UUNAATJB7NN3MUWMJ.json","graph_json":"https://pith.science/api/pith-number/T6X2JUJQ4UUNAATJB7NN3MUWMJ/graph.json","events_json":"https://pith.science/api/pith-number/T6X2JUJQ4UUNAATJB7NN3MUWMJ/events.json","paper":"https://pith.science/paper/T6X2JUJQ"},"agent_actions":{"view_html":"https://pith.science/pith/T6X2JUJQ4UUNAATJB7NN3MUWMJ","download_json":"https://pith.science/pith/T6X2JUJQ4UUNAATJB7NN3MUWMJ.json","view_paper":"https://pith.science/paper/T6X2JUJQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.10589&json=true","fetch_graph":"https://pith.science/api/pith-number/T6X2JUJQ4UUNAATJB7NN3MUWMJ/graph.json","fetch_events":"https://pith.science/api/pith-number/T6X2JUJQ4UUNAATJB7NN3MUWMJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T6X2JUJQ4UUNAATJB7NN3MUWMJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T6X2JUJQ4UUNAATJB7NN3MUWMJ/action/storage_attestation","attest_author":"https://pith.science/pith/T6X2JUJQ4UUNAATJB7NN3MUWMJ/action/author_attestation","sign_citation":"https://pith.science/pith/T6X2JUJQ4UUNAATJB7NN3MUWMJ/action/citation_signature","submit_replication":"https://pith.science/pith/T6X2JUJQ4UUNAATJB7NN3MUWMJ/action/replication_record"}},"created_at":"2026-07-05T11:29:11.442864+00:00","updated_at":"2026-07-05T11:29:11.442864+00:00"}