{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:72IXUQ7GZJTHFJOHYUHKGVFUNJ","short_pith_number":"pith:72IXUQ7G","schema_version":"1.0","canonical_sha256":"fe917a43e6ca6672a5c7c50ea354b46a6d3495175e6a7d3e96d8e302ab11a98c","source":{"kind":"arxiv","id":"1704.02470","version":2},"attestation_state":"computed","paper":{"title":"DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrey Ignatov, Kenneth Vanhoey, Luc Van Gool, Nikolay Kobyshev, Radu Timofte","submitted_at":"2017-04-08T10:27:36Z","abstract_excerpt":"Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations - small sensor size, compact lenses and the lack of specific hardware, - impede them to achieve the quality results of DSLR cameras. In this work we present an end-to-end deep learning approach that bridges this gap by translating ordinary photos into DSLR-quality images. We propose learning the translation function using a residual convolutional neural network that improves both color rendition and image sharpness. Since the standard mean squared loss is not well suited for measuring perceptual imag"},"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":"1704.02470","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-08T10:27:36Z","cross_cats_sorted":[],"title_canon_sha256":"a2345c4710edbf0db4a250bb31e9e6ec09936af0aadb4bef81d614076c8ce212","abstract_canon_sha256":"bde42b6523e761dc8a549bc3956e27bc62c6153790338ca3e1a8c7e77b079021"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:55.282256Z","signature_b64":"lJB8hnPFac5ciOCs+fMCLmtuAKV4AlO+PF9ULnckdKEVjbwSpy18fQNfo7S4OzoCsOqcE3y3x7w0AqwL0hT0AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe917a43e6ca6672a5c7c50ea354b46a6d3495175e6a7d3e96d8e302ab11a98c","last_reissued_at":"2026-05-18T00:35:55.281903Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:55.281903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrey Ignatov, Kenneth Vanhoey, Luc Van Gool, Nikolay Kobyshev, Radu Timofte","submitted_at":"2017-04-08T10:27:36Z","abstract_excerpt":"Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations - small sensor size, compact lenses and the lack of specific hardware, - impede them to achieve the quality results of DSLR cameras. In this work we present an end-to-end deep learning approach that bridges this gap by translating ordinary photos into DSLR-quality images. We propose learning the translation function using a residual convolutional neural network that improves both color rendition and image sharpness. Since the standard mean squared loss is not well suited for measuring perceptual imag"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.02470","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":"1704.02470","created_at":"2026-05-18T00:35:55.281958+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.02470v2","created_at":"2026-05-18T00:35:55.281958+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.02470","created_at":"2026-05-18T00:35:55.281958+00:00"},{"alias_kind":"pith_short_12","alias_value":"72IXUQ7GZJTH","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"72IXUQ7GZJTHFJOH","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"72IXUQ7G","created_at":"2026-05-18T12:31:03.183658+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/72IXUQ7GZJTHFJOHYUHKGVFUNJ","json":"https://pith.science/pith/72IXUQ7GZJTHFJOHYUHKGVFUNJ.json","graph_json":"https://pith.science/api/pith-number/72IXUQ7GZJTHFJOHYUHKGVFUNJ/graph.json","events_json":"https://pith.science/api/pith-number/72IXUQ7GZJTHFJOHYUHKGVFUNJ/events.json","paper":"https://pith.science/paper/72IXUQ7G"},"agent_actions":{"view_html":"https://pith.science/pith/72IXUQ7GZJTHFJOHYUHKGVFUNJ","download_json":"https://pith.science/pith/72IXUQ7GZJTHFJOHYUHKGVFUNJ.json","view_paper":"https://pith.science/paper/72IXUQ7G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.02470&json=true","fetch_graph":"https://pith.science/api/pith-number/72IXUQ7GZJTHFJOHYUHKGVFUNJ/graph.json","fetch_events":"https://pith.science/api/pith-number/72IXUQ7GZJTHFJOHYUHKGVFUNJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/72IXUQ7GZJTHFJOHYUHKGVFUNJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/72IXUQ7GZJTHFJOHYUHKGVFUNJ/action/storage_attestation","attest_author":"https://pith.science/pith/72IXUQ7GZJTHFJOHYUHKGVFUNJ/action/author_attestation","sign_citation":"https://pith.science/pith/72IXUQ7GZJTHFJOHYUHKGVFUNJ/action/citation_signature","submit_replication":"https://pith.science/pith/72IXUQ7GZJTHFJOHYUHKGVFUNJ/action/replication_record"}},"created_at":"2026-05-18T00:35:55.281958+00:00","updated_at":"2026-05-18T00:35:55.281958+00:00"}