{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:JGLAMFKT6BHA63JU5N6MBQKIJP","short_pith_number":"pith:JGLAMFKT","schema_version":"1.0","canonical_sha256":"4996061553f04e0f6d34eb7cc0c1484bc7538bc70c43e0c466fa6347a52634b4","source":{"kind":"arxiv","id":"2204.06979","version":1},"attestation_state":"computed","paper":{"title":"HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Achim Streit, Behnood Rasti, Daniel Coquelin, Markus G\\\"otz, Pedram Ghamisi, Richard Gloaguen","submitted_at":"2022-04-14T14:08:55Z","abstract_excerpt":"As with any physical instrument, hyperspectral cameras induce different kinds of noise in the acquired data. Therefore, Hyperspectral denoising is a crucial step for analyzing hyperspectral images (HSIs). Conventional computational methods rarely use GPUs to improve efficiency and are not fully open-source. Alternatively, deep learning-based methods are often open-source and use GPUs, but their training and utilization for real-world applications remain non-trivial for many researchers. Consequently, we propose HyDe: the first open-source, GPU-accelerated Python-based, hyperspectral image deno"},"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":"2204.06979","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-14T14:08:55Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"9dc15d0e1e4cddae5c194671c41f8215d81d2e840ae3039a25ed6b063652d783","abstract_canon_sha256":"9610d9d740ea988c564eb94e1bb9ffe586731c3cb2dbd59b91ceebde9ae9b4ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:19:14.880478Z","signature_b64":"hk2BUhJ9/r9Ha1awPq8Ws8/U29JlwHQoJeiI6wSIXuBICwNGzIjEiQL0fVBvqL2f4PZCuCIhktyowPca+4woCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4996061553f04e0f6d34eb7cc0c1484bc7538bc70c43e0c466fa6347a52634b4","last_reissued_at":"2026-07-05T05:19:14.880040Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:19:14.880040Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Achim Streit, Behnood Rasti, Daniel Coquelin, Markus G\\\"otz, Pedram Ghamisi, Richard Gloaguen","submitted_at":"2022-04-14T14:08:55Z","abstract_excerpt":"As with any physical instrument, hyperspectral cameras induce different kinds of noise in the acquired data. Therefore, Hyperspectral denoising is a crucial step for analyzing hyperspectral images (HSIs). Conventional computational methods rarely use GPUs to improve efficiency and are not fully open-source. Alternatively, deep learning-based methods are often open-source and use GPUs, but their training and utilization for real-world applications remain non-trivial for many researchers. Consequently, we propose HyDe: the first open-source, GPU-accelerated Python-based, hyperspectral image deno"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.06979","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/2204.06979/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":"2204.06979","created_at":"2026-07-05T05:19:14.880095+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.06979v1","created_at":"2026-07-05T05:19:14.880095+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.06979","created_at":"2026-07-05T05:19:14.880095+00:00"},{"alias_kind":"pith_short_12","alias_value":"JGLAMFKT6BHA","created_at":"2026-07-05T05:19:14.880095+00:00"},{"alias_kind":"pith_short_16","alias_value":"JGLAMFKT6BHA63JU","created_at":"2026-07-05T05:19:14.880095+00:00"},{"alias_kind":"pith_short_8","alias_value":"JGLAMFKT","created_at":"2026-07-05T05:19:14.880095+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/JGLAMFKT6BHA63JU5N6MBQKIJP","json":"https://pith.science/pith/JGLAMFKT6BHA63JU5N6MBQKIJP.json","graph_json":"https://pith.science/api/pith-number/JGLAMFKT6BHA63JU5N6MBQKIJP/graph.json","events_json":"https://pith.science/api/pith-number/JGLAMFKT6BHA63JU5N6MBQKIJP/events.json","paper":"https://pith.science/paper/JGLAMFKT"},"agent_actions":{"view_html":"https://pith.science/pith/JGLAMFKT6BHA63JU5N6MBQKIJP","download_json":"https://pith.science/pith/JGLAMFKT6BHA63JU5N6MBQKIJP.json","view_paper":"https://pith.science/paper/JGLAMFKT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.06979&json=true","fetch_graph":"https://pith.science/api/pith-number/JGLAMFKT6BHA63JU5N6MBQKIJP/graph.json","fetch_events":"https://pith.science/api/pith-number/JGLAMFKT6BHA63JU5N6MBQKIJP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JGLAMFKT6BHA63JU5N6MBQKIJP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JGLAMFKT6BHA63JU5N6MBQKIJP/action/storage_attestation","attest_author":"https://pith.science/pith/JGLAMFKT6BHA63JU5N6MBQKIJP/action/author_attestation","sign_citation":"https://pith.science/pith/JGLAMFKT6BHA63JU5N6MBQKIJP/action/citation_signature","submit_replication":"https://pith.science/pith/JGLAMFKT6BHA63JU5N6MBQKIJP/action/replication_record"}},"created_at":"2026-07-05T05:19:14.880095+00:00","updated_at":"2026-07-05T05:19:14.880095+00:00"}