{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:VIO6GAVGMHPTKBMLDXUJG4WPX3","short_pith_number":"pith:VIO6GAVG","canonical_record":{"source":{"id":"2111.14948","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-11-29T20:52:10Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b8c821ea0f4440643a0f07a9acb6445fb72bcd210a5221af6aec6695b4ebff5d","abstract_canon_sha256":"1e9d72984ca19bf50b4b046750b4590458ec8d28011f23bb37287959b2cf30a9"},"schema_version":"1.0"},"canonical_sha256":"aa1de302a661df35058b1de89372cfbec7ee003aa8ed50ddde151a55de14df47","source":{"kind":"arxiv","id":"2111.14948","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.14948","created_at":"2026-07-05T03:36:20Z"},{"alias_kind":"arxiv_version","alias_value":"2111.14948v1","created_at":"2026-07-05T03:36:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.14948","created_at":"2026-07-05T03:36:20Z"},{"alias_kind":"pith_short_12","alias_value":"VIO6GAVGMHPT","created_at":"2026-07-05T03:36:20Z"},{"alias_kind":"pith_short_16","alias_value":"VIO6GAVGMHPTKBML","created_at":"2026-07-05T03:36:20Z"},{"alias_kind":"pith_short_8","alias_value":"VIO6GAVG","created_at":"2026-07-05T03:36:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:VIO6GAVGMHPTKBMLDXUJG4WPX3","target":"record","payload":{"canonical_record":{"source":{"id":"2111.14948","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-11-29T20:52:10Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b8c821ea0f4440643a0f07a9acb6445fb72bcd210a5221af6aec6695b4ebff5d","abstract_canon_sha256":"1e9d72984ca19bf50b4b046750b4590458ec8d28011f23bb37287959b2cf30a9"},"schema_version":"1.0"},"canonical_sha256":"aa1de302a661df35058b1de89372cfbec7ee003aa8ed50ddde151a55de14df47","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:36:20.896182Z","signature_b64":"3UvG8kqM2waRqxGkWZmGoQYAIO7PfMXJc3H4qRHFjMGwSF+Fj42OTye6fccSLljfZB3E8xlw8YICFRbg2QNSDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa1de302a661df35058b1de89372cfbec7ee003aa8ed50ddde151a55de14df47","last_reissued_at":"2026-07-05T03:36:20.895650Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:36:20.895650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.14948","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:36:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2icB6pT4U7vcV4V1zULEeWL+cpXDCzAjbK0hCaGOFDID6upEYNI26JUBPs8SFYrj9TPQQf2NpLJOL/DTDx+GCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:41:44.971888Z"},"content_sha256":"df659f1957b98500633378f8b0ea7e6ac22659dfb87d26ce260ecfab4973290c","schema_version":"1.0","event_id":"sha256:df659f1957b98500633378f8b0ea7e6ac22659dfb87d26ce260ecfab4973290c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:VIO6GAVGMHPTKBMLDXUJG4WPX3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Image denoising by Super Neurons: Why go deep?","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Junaid Malik, Moncef Gabbouj, Serkan Kiranyaz","submitted_at":"2021-11-29T20:52:10Z","abstract_excerpt":"Classical image denoising methods utilize the non-local self-similarity principle to effectively recover image content from noisy images. Current state-of-the-art methods use deep convolutional neural networks (CNNs) to effectively learn the mapping from noisy to clean images. Deep denoising CNNs manifest a high learning capacity and integrate non-local information owing to the large receptive field yielded by numerous cascade of hidden layers. However, deep networks are also computationally complex and require large data for training. To address these issues, this study draws the focus on the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.14948","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/2111.14948/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:36:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cxq3ikXCXZEnOEplLatsdH70lTYxEPONjWyLBVs42cMx/HxfJTYG5di4bb0y9Q98/1nR88vSUfwYsHGW+kfACQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:41:44.972265Z"},"content_sha256":"11e107cf73408bb53283b85fb72cb9458bf4029bb70a354f212fddac77cb89b9","schema_version":"1.0","event_id":"sha256:11e107cf73408bb53283b85fb72cb9458bf4029bb70a354f212fddac77cb89b9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VIO6GAVGMHPTKBMLDXUJG4WPX3/bundle.json","state_url":"https://pith.science/pith/VIO6GAVGMHPTKBMLDXUJG4WPX3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VIO6GAVGMHPTKBMLDXUJG4WPX3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T18:41:44Z","links":{"resolver":"https://pith.science/pith/VIO6GAVGMHPTKBMLDXUJG4WPX3","bundle":"https://pith.science/pith/VIO6GAVGMHPTKBMLDXUJG4WPX3/bundle.json","state":"https://pith.science/pith/VIO6GAVGMHPTKBMLDXUJG4WPX3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VIO6GAVGMHPTKBMLDXUJG4WPX3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:VIO6GAVGMHPTKBMLDXUJG4WPX3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1e9d72984ca19bf50b4b046750b4590458ec8d28011f23bb37287959b2cf30a9","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-11-29T20:52:10Z","title_canon_sha256":"b8c821ea0f4440643a0f07a9acb6445fb72bcd210a5221af6aec6695b4ebff5d"},"schema_version":"1.0","source":{"id":"2111.14948","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.14948","created_at":"2026-07-05T03:36:20Z"},{"alias_kind":"arxiv_version","alias_value":"2111.14948v1","created_at":"2026-07-05T03:36:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.14948","created_at":"2026-07-05T03:36:20Z"},{"alias_kind":"pith_short_12","alias_value":"VIO6GAVGMHPT","created_at":"2026-07-05T03:36:20Z"},{"alias_kind":"pith_short_16","alias_value":"VIO6GAVGMHPTKBML","created_at":"2026-07-05T03:36:20Z"},{"alias_kind":"pith_short_8","alias_value":"VIO6GAVG","created_at":"2026-07-05T03:36:20Z"}],"graph_snapshots":[{"event_id":"sha256:11e107cf73408bb53283b85fb72cb9458bf4029bb70a354f212fddac77cb89b9","target":"graph","created_at":"2026-07-05T03:36:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2111.14948/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Classical image denoising methods utilize the non-local self-similarity principle to effectively recover image content from noisy images. Current state-of-the-art methods use deep convolutional neural networks (CNNs) to effectively learn the mapping from noisy to clean images. Deep denoising CNNs manifest a high learning capacity and integrate non-local information owing to the large receptive field yielded by numerous cascade of hidden layers. However, deep networks are also computationally complex and require large data for training. To address these issues, this study draws the focus on the","authors_text":"Junaid Malik, Moncef Gabbouj, Serkan Kiranyaz","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-11-29T20:52:10Z","title":"Image denoising by Super Neurons: Why go deep?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.14948","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:df659f1957b98500633378f8b0ea7e6ac22659dfb87d26ce260ecfab4973290c","target":"record","created_at":"2026-07-05T03:36:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1e9d72984ca19bf50b4b046750b4590458ec8d28011f23bb37287959b2cf30a9","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-11-29T20:52:10Z","title_canon_sha256":"b8c821ea0f4440643a0f07a9acb6445fb72bcd210a5221af6aec6695b4ebff5d"},"schema_version":"1.0","source":{"id":"2111.14948","kind":"arxiv","version":1}},"canonical_sha256":"aa1de302a661df35058b1de89372cfbec7ee003aa8ed50ddde151a55de14df47","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aa1de302a661df35058b1de89372cfbec7ee003aa8ed50ddde151a55de14df47","first_computed_at":"2026-07-05T03:36:20.895650Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:36:20.895650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3UvG8kqM2waRqxGkWZmGoQYAIO7PfMXJc3H4qRHFjMGwSF+Fj42OTye6fccSLljfZB3E8xlw8YICFRbg2QNSDg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:36:20.896182Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.14948","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:df659f1957b98500633378f8b0ea7e6ac22659dfb87d26ce260ecfab4973290c","sha256:11e107cf73408bb53283b85fb72cb9458bf4029bb70a354f212fddac77cb89b9"],"state_sha256":"654cf26b3710e512d3ac0b630dbb66b1cfea468bf00b4fc8eced452b21bdb162"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"65ctuexPHtuHwFZcFv86AIl3lTRn2mcPMbG6nIYwNBzO0coQVdRhytPr3ef6we0aA6krun9mXi822C3m+Jm0AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T18:41:44.974279Z","bundle_sha256":"28ef6876a239a31349ee751c7997a228c7e056273e17a391578f3fac1540ca4f"}}