{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:Q72DFYQN5VMOHF7S53ODR2AD65","short_pith_number":"pith:Q72DFYQN","schema_version":"1.0","canonical_sha256":"87f432e20ded58e397f2eedc38e803f7566fa5d24d05f81cd96c00b116412364","source":{"kind":"arxiv","id":"1903.09520","version":1},"attestation_state":"computed","paper":{"title":"A lightweight convolutional neural network for image denoising with fine details preservation capability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Avisek Lahiri, Prabir Kumar Biswas, Sutanu Bera","submitted_at":"2019-03-22T14:14:39Z","abstract_excerpt":"Image denoising is a fundamental problem in image processing whose primary objective is to remove the noise while preserving the original image structure. In this work, we proposed a new architecture for image denoising. We have used several dense blocks to design our network. Additionally, we have forwarded feature extracted in the first layer to the input of every transition layer. Our experimental result suggests that the use of low-level feature helps in reconstructing better texture. Furthermore, we had trained our network with a combination of MSE and a differentiable multi-scale structu"},"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":"1903.09520","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-03-22T14:14:39Z","cross_cats_sorted":[],"title_canon_sha256":"65703cfd3b5fe7a8ddc5d7fd4b4e69a3aa245cfbae02d5b757b27ebaee6ead0c","abstract_canon_sha256":"282a45e0e4a4d15b8164e1c070041ecfb109e803439ca8a82a97b3588630937a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:39.407578Z","signature_b64":"aRQYrmYLI45foXk+Pw1UMClD9+AmgDUB1JWkMCb8VKTOoZTa/oKTGqIw17FxnxdOt6xvOmpxIHAxOeTjjicECw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"87f432e20ded58e397f2eedc38e803f7566fa5d24d05f81cd96c00b116412364","last_reissued_at":"2026-05-17T23:50:39.407047Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:39.407047Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A lightweight convolutional neural network for image denoising with fine details preservation capability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Avisek Lahiri, Prabir Kumar Biswas, Sutanu Bera","submitted_at":"2019-03-22T14:14:39Z","abstract_excerpt":"Image denoising is a fundamental problem in image processing whose primary objective is to remove the noise while preserving the original image structure. In this work, we proposed a new architecture for image denoising. We have used several dense blocks to design our network. Additionally, we have forwarded feature extracted in the first layer to the input of every transition layer. Our experimental result suggests that the use of low-level feature helps in reconstructing better texture. Furthermore, we had trained our network with a combination of MSE and a differentiable multi-scale structu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.09520","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":""},"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":"1903.09520","created_at":"2026-05-17T23:50:39.407123+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.09520v1","created_at":"2026-05-17T23:50:39.407123+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.09520","created_at":"2026-05-17T23:50:39.407123+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q72DFYQN5VMO","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q72DFYQN5VMOHF7S","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q72DFYQN","created_at":"2026-05-18T12:33:27.125529+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/Q72DFYQN5VMOHF7S53ODR2AD65","json":"https://pith.science/pith/Q72DFYQN5VMOHF7S53ODR2AD65.json","graph_json":"https://pith.science/api/pith-number/Q72DFYQN5VMOHF7S53ODR2AD65/graph.json","events_json":"https://pith.science/api/pith-number/Q72DFYQN5VMOHF7S53ODR2AD65/events.json","paper":"https://pith.science/paper/Q72DFYQN"},"agent_actions":{"view_html":"https://pith.science/pith/Q72DFYQN5VMOHF7S53ODR2AD65","download_json":"https://pith.science/pith/Q72DFYQN5VMOHF7S53ODR2AD65.json","view_paper":"https://pith.science/paper/Q72DFYQN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.09520&json=true","fetch_graph":"https://pith.science/api/pith-number/Q72DFYQN5VMOHF7S53ODR2AD65/graph.json","fetch_events":"https://pith.science/api/pith-number/Q72DFYQN5VMOHF7S53ODR2AD65/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q72DFYQN5VMOHF7S53ODR2AD65/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q72DFYQN5VMOHF7S53ODR2AD65/action/storage_attestation","attest_author":"https://pith.science/pith/Q72DFYQN5VMOHF7S53ODR2AD65/action/author_attestation","sign_citation":"https://pith.science/pith/Q72DFYQN5VMOHF7S53ODR2AD65/action/citation_signature","submit_replication":"https://pith.science/pith/Q72DFYQN5VMOHF7S53ODR2AD65/action/replication_record"}},"created_at":"2026-05-17T23:50:39.407123+00:00","updated_at":"2026-05-17T23:50:39.407123+00:00"}