{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:NORWAHFBAB5NSTSDOAVX2DFIMO","short_pith_number":"pith:NORWAHFB","schema_version":"1.0","canonical_sha256":"6ba3601ca1007ad94e43702b7d0ca86397e2c0a11b51b77d230f64dec0934a05","source":{"kind":"arxiv","id":"2207.00164","version":2},"attestation_state":"computed","paper":{"title":"Deep Optical Coding Design in Computational Imaging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Balpreet Singh Ahluwalia, Chamira U. S. Edussooriya, Dushan N. Wadduwage, Edwin Vargas, Hans Garcia, Hasindu Kariyawasam, Henry Arguello, Jorge Bacca, Kithmini Herath, Miguel Marquez, Peter So, Ramith Hettiarachchi, Udith Haputhanthri","submitted_at":"2022-06-27T04:41:48Z","abstract_excerpt":"Computational optical imaging (COI) systems leverage optical coding elements (CE) in their setups to encode a high-dimensional scene in a single or multiple snapshots and decode it by using computational algorithms. The performance of COI systems highly depends on the design of its main components: the CE pattern and the computational method used to perform a given task. Conventional approaches rely on random patterns or analytical designs to set the distribution of the CE. However, the available data and algorithm capabilities of deep neural networks (DNNs) have opened a new horizon in CE dat"},"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":"2207.00164","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-27T04:41:48Z","cross_cats_sorted":[],"title_canon_sha256":"879f73d08f42ca6bffa67dfa9229613b832ba3f36e546bd81a6c7fda9f87b5ac","abstract_canon_sha256":"9350e1a99c87af28433a9e4f620f14614442bd81b071f2aa3e63054c0c889888"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:49:23.613353Z","signature_b64":"Wix9qeEXbYWWqy8XdUNNVIYMfK8K0gq5yNNP2B6zwPfMkFl3gEPHwwFdnlalmFDl4FYd1uszydOaLJGvqJisCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ba3601ca1007ad94e43702b7d0ca86397e2c0a11b51b77d230f64dec0934a05","last_reissued_at":"2026-07-05T04:49:23.612851Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:49:23.612851Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Optical Coding Design in Computational Imaging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Balpreet Singh Ahluwalia, Chamira U. S. Edussooriya, Dushan N. Wadduwage, Edwin Vargas, Hans Garcia, Hasindu Kariyawasam, Henry Arguello, Jorge Bacca, Kithmini Herath, Miguel Marquez, Peter So, Ramith Hettiarachchi, Udith Haputhanthri","submitted_at":"2022-06-27T04:41:48Z","abstract_excerpt":"Computational optical imaging (COI) systems leverage optical coding elements (CE) in their setups to encode a high-dimensional scene in a single or multiple snapshots and decode it by using computational algorithms. The performance of COI systems highly depends on the design of its main components: the CE pattern and the computational method used to perform a given task. Conventional approaches rely on random patterns or analytical designs to set the distribution of the CE. However, the available data and algorithm capabilities of deep neural networks (DNNs) have opened a new horizon in CE dat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.00164","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2207.00164/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":"2207.00164","created_at":"2026-07-05T04:49:23.612906+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.00164v2","created_at":"2026-07-05T04:49:23.612906+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.00164","created_at":"2026-07-05T04:49:23.612906+00:00"},{"alias_kind":"pith_short_12","alias_value":"NORWAHFBAB5N","created_at":"2026-07-05T04:49:23.612906+00:00"},{"alias_kind":"pith_short_16","alias_value":"NORWAHFBAB5NSTSD","created_at":"2026-07-05T04:49:23.612906+00:00"},{"alias_kind":"pith_short_8","alias_value":"NORWAHFB","created_at":"2026-07-05T04:49:23.612906+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/NORWAHFBAB5NSTSDOAVX2DFIMO","json":"https://pith.science/pith/NORWAHFBAB5NSTSDOAVX2DFIMO.json","graph_json":"https://pith.science/api/pith-number/NORWAHFBAB5NSTSDOAVX2DFIMO/graph.json","events_json":"https://pith.science/api/pith-number/NORWAHFBAB5NSTSDOAVX2DFIMO/events.json","paper":"https://pith.science/paper/NORWAHFB"},"agent_actions":{"view_html":"https://pith.science/pith/NORWAHFBAB5NSTSDOAVX2DFIMO","download_json":"https://pith.science/pith/NORWAHFBAB5NSTSDOAVX2DFIMO.json","view_paper":"https://pith.science/paper/NORWAHFB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.00164&json=true","fetch_graph":"https://pith.science/api/pith-number/NORWAHFBAB5NSTSDOAVX2DFIMO/graph.json","fetch_events":"https://pith.science/api/pith-number/NORWAHFBAB5NSTSDOAVX2DFIMO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NORWAHFBAB5NSTSDOAVX2DFIMO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NORWAHFBAB5NSTSDOAVX2DFIMO/action/storage_attestation","attest_author":"https://pith.science/pith/NORWAHFBAB5NSTSDOAVX2DFIMO/action/author_attestation","sign_citation":"https://pith.science/pith/NORWAHFBAB5NSTSDOAVX2DFIMO/action/citation_signature","submit_replication":"https://pith.science/pith/NORWAHFBAB5NSTSDOAVX2DFIMO/action/replication_record"}},"created_at":"2026-07-05T04:49:23.612906+00:00","updated_at":"2026-07-05T04:49:23.612906+00:00"}