{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:UMKYT2EAS3ODSTEBAOBMKTD7SM","short_pith_number":"pith:UMKYT2EA","schema_version":"1.0","canonical_sha256":"a31589e88096dc394c810382c54c7f932bc7b873b3adf883d90633675534f125","source":{"kind":"arxiv","id":"1305.0871","version":2},"attestation_state":"computed","paper":{"title":"Dictionary learning based image enhancement for rarity detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hui Li, Weifeng Liu, Xiaomeng Wang, Yanjiang Wang","submitted_at":"2013-05-04T03:14:46Z","abstract_excerpt":"Image enhancement is an important image processing technique that processes images suitably for a specific application e.g. image editing. The conventional solutions of image enhancement are grouped into two categories which are spatial domain processing method and transform domain processing method such as contrast manipulation, histogram equalization, homomorphic filtering. This paper proposes a new image enhance method based on dictionary learning. Particularly, the proposed method adjusts the image by manipulating the rarity of dictionary atoms. Firstly, learn the dictionary through sparse"},"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":"1305.0871","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-05-04T03:14:46Z","cross_cats_sorted":[],"title_canon_sha256":"6f2758ff9fc1afc910460a762cd28631be4f17ded21c747a704e83e6567bf286","abstract_canon_sha256":"c99baefdd71e8f36fdda4413ac6e43d5deb4aeaaa66a0362861f9642d0532ee5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:04:46.125751Z","signature_b64":"YCDAFmu9eTYskYKh5HxmHOoru2VgZM5IICBJd5ubuyRkJhI5cCzzLtAonNEJTclmVeAIJHOon5Iufj8u0u/pCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a31589e88096dc394c810382c54c7f932bc7b873b3adf883d90633675534f125","last_reissued_at":"2026-05-18T01:04:46.125108Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:04:46.125108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dictionary learning based image enhancement for rarity detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hui Li, Weifeng Liu, Xiaomeng Wang, Yanjiang Wang","submitted_at":"2013-05-04T03:14:46Z","abstract_excerpt":"Image enhancement is an important image processing technique that processes images suitably for a specific application e.g. image editing. The conventional solutions of image enhancement are grouped into two categories which are spatial domain processing method and transform domain processing method such as contrast manipulation, histogram equalization, homomorphic filtering. This paper proposes a new image enhance method based on dictionary learning. Particularly, the proposed method adjusts the image by manipulating the rarity of dictionary atoms. Firstly, learn the dictionary through sparse"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1305.0871","kind":"arxiv","version":2},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1305.0871","created_at":"2026-05-18T01:04:46.125183+00:00"},{"alias_kind":"arxiv_version","alias_value":"1305.0871v2","created_at":"2026-05-18T01:04:46.125183+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1305.0871","created_at":"2026-05-18T01:04:46.125183+00:00"},{"alias_kind":"pith_short_12","alias_value":"UMKYT2EAS3OD","created_at":"2026-05-18T12:28:02.375192+00:00"},{"alias_kind":"pith_short_16","alias_value":"UMKYT2EAS3ODSTEB","created_at":"2026-05-18T12:28:02.375192+00:00"},{"alias_kind":"pith_short_8","alias_value":"UMKYT2EA","created_at":"2026-05-18T12:28:02.375192+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/UMKYT2EAS3ODSTEBAOBMKTD7SM","json":"https://pith.science/pith/UMKYT2EAS3ODSTEBAOBMKTD7SM.json","graph_json":"https://pith.science/api/pith-number/UMKYT2EAS3ODSTEBAOBMKTD7SM/graph.json","events_json":"https://pith.science/api/pith-number/UMKYT2EAS3ODSTEBAOBMKTD7SM/events.json","paper":"https://pith.science/paper/UMKYT2EA"},"agent_actions":{"view_html":"https://pith.science/pith/UMKYT2EAS3ODSTEBAOBMKTD7SM","download_json":"https://pith.science/pith/UMKYT2EAS3ODSTEBAOBMKTD7SM.json","view_paper":"https://pith.science/paper/UMKYT2EA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1305.0871&json=true","fetch_graph":"https://pith.science/api/pith-number/UMKYT2EAS3ODSTEBAOBMKTD7SM/graph.json","fetch_events":"https://pith.science/api/pith-number/UMKYT2EAS3ODSTEBAOBMKTD7SM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UMKYT2EAS3ODSTEBAOBMKTD7SM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UMKYT2EAS3ODSTEBAOBMKTD7SM/action/storage_attestation","attest_author":"https://pith.science/pith/UMKYT2EAS3ODSTEBAOBMKTD7SM/action/author_attestation","sign_citation":"https://pith.science/pith/UMKYT2EAS3ODSTEBAOBMKTD7SM/action/citation_signature","submit_replication":"https://pith.science/pith/UMKYT2EAS3ODSTEBAOBMKTD7SM/action/replication_record"}},"created_at":"2026-05-18T01:04:46.125183+00:00","updated_at":"2026-05-18T01:04:46.125183+00:00"}