{"as_of":"2026-08-21T19:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f3078533df4fe02f367cadeb027f659cc324f403898c919c3eb0c08f4dcb1ca4","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:08:54.629908Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2504.16958/citation-record","integrity":"/paper/2504.16958/integrity","json":"/paper/2504.16958/citation-record.json","paper":"/paper/2504.16958"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:59.015147Z","title":"Cross-scope spatial-spectral informa- tion aggregation for hyperspectral image super-resolution,","venue":null,"work_id":"caa740fb-aba9-4cff-918b-caa8548f8b39","year":2024},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:52.920316Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:cb190345cc5dcac1650d6ba96c4e6556f8d9a152e78701b847680a892221939c","observation_id":"d325034e-8e6b-4d5f-ac97-9e97b6a1ddc4","resolution":{"observed_at":"2026-08-16T11:08:59.066512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:58.979523Z","title":"Hyperspectral image super-resolution based on spatial-spectral feature extraction network,","venue":null,"work_id":"204d5212-02c0-4f56-9ea3-c0ba1b0f5e07","year":2023},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:52.979780Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:c1aa2d88a170c21e60fff756e8d08ceb62bee127ea133297f17bf6f4a6a3534a","observation_id":"57384596-e4e7-4780-8774-2b4c0bc7fc28","resolution":{"observed_at":"2026-08-16T11:08:59.003285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:58.916724Z","title":"Nonlocal low-rank tensor completion for visual data,","venue":null,"work_id":"8cf77ee2-3d01-4421-b5af-fae758a03dfe","year":2021},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.014063Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:ccb7009ff67d14f96005bcbaf34f061623492baa34298d4f06db7d11a688a576","observation_id":"5c6115ea-d724-4b7e-9f60-fd0abd36b8f9","resolution":{"observed_at":"2026-08-16T11:08:58.948613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:58.877182Z","title":"Image super-resolution using deep convolutional networks,","venue":null,"work_id":"be7f95f8-1c71-453f-8022-5d7f954a42f8","year":2016},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.088907Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:574cd13b53af23512efab3bdfd133a8cbd1028b2b0ddaa7cbe51ae60cf91e4e5","observation_id":"e90f87f4-ef5f-402e-8957-8c62421b4bb4","resolution":{"observed_at":"2026-08-16T11:08:58.905220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:58.800075Z","title":"Accurate image super-resolution using very deep convolutional networks,","venue":null,"work_id":"16ec8f1f-3d10-4846-acf3-732bd0243e7a","year":2016},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.136082Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:294a61d9b2a85f32729be3d39392bd6c0009c0afcb0bcaf569608e9bca3386a1","observation_id":"9a345bf2-9683-443c-b025-7fc3c8a9f289","resolution":{"observed_at":"2026-08-16T11:08:58.850220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:58.746141Z","title":"Super-resolution reconstruction of knee magnetic resonance imaging based on deep learning,","venue":null,"work_id":"6c3c7780-464e-42b6-ace7-f9beeb987734","year":2020},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.141427Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:032b5e2952c6815d163dee1a14b7a07acd729b4505183487abf91650c7dd795c","observation_id":"4edda870-d3b6-4d37-8a7a-e612e0921d84","resolution":{"observed_at":"2026-08-16T11:08:58.788900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:58.615474Z","title":"A trusted medical image super-resolution method based on feedback adaptive weighted dense network,","venue":null,"work_id":"1104213b-bf2c-458d-8505-42322e4ec20e","year":2020},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.197994Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:677dab0efa007a56ed8ec59908a92207ebdfae9bed7d353b3bce5b165b718d03","observation_id":"01293ead-77ce-4303-9fe6-b44dfcb548bf","resolution":{"observed_at":"2026-08-16T11:08:58.668915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:58.511376Z","title":"Task transformer network for joint MRI reconstruction and super-resolution,","venue":null,"work_id":"a3673f5f-64e1-455d-b8fd-738d5e5e16f8","year":2021},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.218841Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:4b7c0cb9a2a9e658a908ff8b0eac7a5f338b5e86203098b53eba6309d0865616","observation_id":"b7062888-82e5-4b4b-939c-c850df069298","resolution":{"observed_at":"2026-08-16T11:08:58.584622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:58.417559Z","title":"Enhanced deep residual networks for single image super-resolution,","venue":null,"work_id":"002f4405-efbe-44a1-b445-75e427dc40ec","year":2017},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.280907Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:fd27affa817ddb4ebb9a8bba42450f8d2dffd9bdbe960f51dadcb5bf35cb3e28","observation_id":"ad27df56-7711-4236-bbad-7b1e7f927dcf","resolution":{"observed_at":"2026-08-16T11:08:58.499837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:58.242854Z","title":"PET image super- resolution using generative adversarial networks,","venue":null,"work_id":"6c95b70b-418f-4f66-80ec-76f961fd4b40","year":2020},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.285444Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:592dbbc895a80f35269056e5120a133caf33df0aae6773dc6779609c00b250f8","observation_id":"09081069-7345-49d5-a9fd-75d857f33a4a","resolution":{"observed_at":"2026-08-16T11:08:58.364926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:58.143419Z","title":"Arbitrary- scale image super-resolution via degradation perception,","venue":null,"work_id":"ac4baafb-9d91-48dd-97f4-962166a42872","year":2024},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.291568Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:71569102ead3f59dba938a4fd43f156a908e06e99711d1717b116e33f96ea7b1","observation_id":"4d9de987-6726-42c4-b37c-d55ca53922ce","resolution":{"observed_at":"2026-08-16T11:08:58.194283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:58.041432Z","title":"Single image superresolu- tion based on gradient profile sharpness,","venue":null,"work_id":"bf7821b3-3df2-40ce-9d29-88797e1cc23a","year":2015},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.297080Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:a6589666fc3ac82fa6890d2c1ead8ce85edb4a9e4a0ef4dc5b9ea66dfdd50352","observation_id":"9678a29c-0340-4f85-986c-33f6af872567","resolution":{"observed_at":"2026-08-16T11:08:58.130515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:57.905563Z","title":"Transmrsr: transformer-based self-distilled generative prior for brain MRI super-resolution,","venue":null,"work_id":"ac9c0d02-a653-4771-8d8d-b31c45db3923","year":2023},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.301867Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:68234fea414ef5db8f8f8a410e1edc0aa8548e65c20dcfdbef29d207618f0617","observation_id":"09eaadef-93ca-45fe-9c5f-59fd1840a0b8","resolution":{"observed_at":"2026-08-16T11:08:58.029202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:57.808973Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":"413c7b96-0da9-454e-b8a3-9cb46b659719","year":2016},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.322595Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:bb38f2de328c22343d1ef9235f5cf83c461c3f85b762b63e1c482e4e341904bb","observation_id":"2e961eef-cf1b-4174-a67d-03d77d2e5280","resolution":{"observed_at":"2026-08-16T11:08:57.814487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:57.605539Z","title":"An image is worth 16x16 words: Trans- formers for image recognition at scale,","venue":null,"work_id":"0600aa1f-159f-464e-9d62-f61c78297388","year":2021},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.356089Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:da63c0f6c574612df98aebe2c7837be40d50ba6cc2ba4cfab3c71757064dbf55","observation_id":"af9d8f74-6736-44c7-ab7b-bffb0f46e292","resolution":{"observed_at":"2026-08-16T11:08:57.705013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:57.496322Z","title":"Combining recurrent, convolutional, and continuous-time models with linear state space layers,","venue":null,"work_id":"c41d2603-1f34-4e24-9c0c-e2c16f4698ec","year":2021},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.362130Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:b4148f678c57b410d29c6146047a3212af61a34afe422ffda991457cc0f75e17","observation_id":"db96a847-42fd-4adc-b3e1-894ab5c521c2","resolution":{"observed_at":"2026-08-16T11:08:57.568623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:57.349971Z","title":"Efficiently modeling long sequences with structured state spaces,","venue":null,"work_id":"66c927c1-eafe-43ba-b2b2-8b9af858b460","year":2022},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.435256Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:403978519b4abaf56e8fd998ee68f6ad27d160a515bb1d8bb3e79acf4092ce07","observation_id":"ed53793f-37cf-4241-8c32-d9e054ed620d","resolution":{"observed_at":"2026-08-16T11:08:57.387425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-08-17T20:47:46.242385Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-16T11:08:53.446878Z","title":"Mamba: Linear-time sequence modeling with selective state spaces,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.446878Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:5b951276b0ed9c911e9c7c498a44d012b5f451aa22cb335e82bdff59e6409a26","observation_id":"b27a45ea-db5a-43d4-b41d-45c2aec785b2","resolution":{"observed_at":"2026-08-16T11:08:53.446878Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.13560","last_updated":"2024-09-15T13:26:45Z","snapshot_observed_at":"2026-08-20T09:05:25.237602Z","submitted_at":"2024-01-24T16:17:23Z","title":"SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.13560","snapshot_observed_at":"2026-08-16T11:08:53.452712Z","title":"Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.452712Z"},"links":{"cited_paper":"/paper/2401.13560","citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:2a5ea7c8285af0d772ebc244295d2358772bfe17ff1909d43e0accf4ec078688","observation_id":"78b66f9f-43cf-4c4f-8bb4-9ee3c4c1d141","resolution":{"observed_at":"2026-08-16T11:08:53.452712Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02491","last_updated":"2024-11-08T11:56:04Z","snapshot_observed_at":"2026-08-19T13:18:52.267019Z","submitted_at":"2024-02-04T13:37:21Z","title":"VM-UNet: Vision Mamba UNet for Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02491","snapshot_observed_at":"2026-08-16T11:08:53.501931Z","title":"Vm-unet: Vision mamba unet for medical image segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.501931Z"},"links":{"cited_paper":"/paper/2402.02491","citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:57fdbcfde4442f7fc31b7389d0eee47bb156ad29d8fadfc9f9bebc2a527ef76b","observation_id":"7b9ddea4-767b-496b-9ea8-988a49b03ce1","resolution":{"observed_at":"2026-08-16T11:08:53.501931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09417","last_updated":"2024-11-14T02:00:33Z","snapshot_observed_at":"2026-08-14T11:12:31.002605Z","submitted_at":"2024-01-17T18:56:18Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09417","snapshot_observed_at":"2026-08-16T11:08:53.558031Z","title":"Vision mamba: Efficient visual representation learning with bidirectional state space model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.558031Z"},"links":{"cited_paper":"/paper/2401.09417","citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:ec18aa5379b3d97ccd1da8636004a521fe8b95709346bf26a0e0514ccf4f8e42","observation_id":"ac8bb0f4-85fd-498d-adbc-54e202376f8f","resolution":{"observed_at":"2026-08-16T11:08:53.558031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:57.325909Z","title":"Selective structured state-spaces for long-form video understanding,","venue":null,"work_id":"10cd78f6-b27c-43e4-b477-f8036dddc1c9","year":2023},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.564411Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:cc5f680edd252e4af0fbda115322b5ebdb635563e7d6fff22a9bcd2163fb27ff","observation_id":"1e129d6a-a819-44b0-991b-65924697beca","resolution":{"observed_at":"2026-08-16T11:08:57.337778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10166","last_updated":"2024-12-29T14:57:13Z","snapshot_observed_at":"2026-08-17T14:56:56.233298Z","submitted_at":"2024-01-18T17:55:39Z","title":"VMamba: Visual State Space Model","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10166","snapshot_observed_at":"2026-08-16T11:08:53.569669Z","title":"Vmamba: Visual state space model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.569669Z"},"links":{"cited_paper":"/paper/2401.10166","citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:cd2684f108d26180e9bf4ccb77205f61b0ac1008c62244859114a02437f09ecd","observation_id":"0a49fc6a-806c-46a7-9f3b-e3b4e5a96f46","resolution":{"observed_at":"2026-08-16T11:08:53.569669Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:57.292531Z","title":"Cubic convolution interpolation for digital image processing,","venue":null,"work_id":"2bebd689-06e3-4a0a-87e6-b4db79d9c605","year":1981},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.612680Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:411974e3499ee66915a5db814b788ae2d3097bb8ed558e03916fe684673fbc69","observation_id":"91b1a728-2678-4a4d-8e34-5b5884aa00b7","resolution":{"observed_at":"2026-08-16T11:08:57.297713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:57.213115Z","title":"High-resolution image recovery from image- plane arrays, using convex projections,","venue":null,"work_id":"cf419489-43f2-4b3a-9022-1fdaf4e9d704","year":1989},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.618509Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:d03ceb7a411bccc62b6d9341918102ed40d74a40eda0ba5955f81149e8bc14b6","observation_id":"51bd46ce-6e21-4b2d-b5a2-8dbc31340a64","resolution":{"observed_at":"2026-08-16T11:08:57.219166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:57.187444Z","title":"Lrtv: Mr image super-resolution with low-rank and total variation regularizations,","venue":null,"work_id":"770bce88-65b3-4d71-9c6e-fc531c39cb80","year":2015},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.673032Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:7437634231936dd9e8a4a58d8f07efce8cc28e552dbdedb4f6d16f93dfac988c","observation_id":"239de5a4-ca22-43d7-ba79-3cefe3e8cf5e","resolution":{"observed_at":"2026-08-16T11:08:57.202254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:57.077611Z","title":"Coupled dictionary training for image super-resolution,","venue":null,"work_id":"be16de4a-c5d2-4ee1-b6b7-92d22ea5a3a8","year":2012},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.737836Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:4502ba2ad7d75eab35cb811eea03a972dd2bf1b2d5ee62ac3a2fcaf50f586612","observation_id":"fe09ad55-0364-4058-8c73-ce8240bc5d02","resolution":{"observed_at":"2026-08-16T11:08:57.155694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:53.808483Z","title":"Image super-resolution via sparse representation,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.808483Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:69574bf58c04719681936b333546cf5f2f3812db1e96f74de3dfc6ac9be33920","observation_id":"074c186b-ee70-4430-9e21-3b92776469ce","resolution":{"observed_at":"2026-08-16T11:08:53.808483Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:56.942267Z","title":"Mapanet: A multi-scale attention-guided progressive aggregation network for multi-contrast MRI super-resolution,","venue":null,"work_id":"db5d4458-82db-48c7-8594-847e71e52732","year":2024},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.848996Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:28e639bffb9ce8993276623c30ed0a46de45fbc72237a0b49cb6485ed50a22d7","observation_id":"ec1e5082-b273-4523-bd2f-139682222841","resolution":{"observed_at":"2026-08-16T11:08:56.980380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:56.893450Z","title":"Cytopathology image super-resolution of portable microscope based on convolutional window-integration transformer,","venue":null,"work_id":"aed86768-c285-470c-82b3-795c0bdeafeb","year":2025},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.855209Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:8dc5ec7716e441e15abbad9099e272b06d241c4664edc422fb640434d412c2ca","observation_id":"96caeaff-55a8-46fd-99be-a5947b45903b","resolution":{"observed_at":"2026-08-16T11:08:56.899139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:56.838868Z","title":"A fast medical image super resolution method based on deep learning network,","venue":null,"work_id":"827befd0-f13b-4554-8445-b3eac40b5e36","year":2019},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.860629Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:67f5932facef2d496d7e88bcf0731b2b5ef08e2187e2e5710cbaef3a0ff2da69","observation_id":"c82a6f21-8cd5-4e7a-a88a-2ce021c6e5b2","resolution":{"observed_at":"2026-08-16T11:08:56.882045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:56.733134Z","title":"Photo-realistic single image super-resolution using a generative adversarial network,","venue":null,"work_id":"53e0aa58-3779-437b-8e52-48f839fb775c","year":2017},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.933426Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:c8f39cea6cf81e15f539c1dac17e28843416dc2badd89749e2d50b3c5badfe58","observation_id":"2f8fd66e-b4b0-4d19-b76d-fbcb422f2537","resolution":{"observed_at":"2026-08-16T11:08:56.756045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:56.572294Z","title":"MFTN: multi-level feature transfer network based on mri-transformer for MR image super-resolution,","venue":null,"work_id":"cbb13d1c-4695-4412-a940-6dbb8b1de883","year":2024},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.939444Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:35f37de96c05b704000cbd806c8cc57c468fa6f72bfecd3cfc1df60e58ecb96c","observation_id":"2b66205d-5f7a-441a-a1fc-6670a6a39b04","resolution":{"observed_at":"2026-08-16T11:08:56.654302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:56.483186Z","title":"Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity,","venue":null,"work_id":"dd284000-22d6-4c02-b9c4-3ea7fd76ac7b","year":2017},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:53.975209Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:23a9dc43cb3af459d728df7fc193af51e3c2af4e1feeba369be41d0db7ab5316","observation_id":"b48a56f2-da84-40ea-afce-7663d585aef3","resolution":{"observed_at":"2026-08-16T11:08:56.523046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:56.387341Z","title":"Wavelet-aware transformer network for multi-contrast knee MRI super-resolution,","venue":null,"work_id":"7e340991-d8e1-4107-9994-0d4d5ccf9702","year":2023},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.056229Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:463558cf1ae5316e52b95147f06a48765703bc8f2ef4e0b5c3c0a1605fcd54fb","observation_id":"5a63161e-f8d1-46ad-b986-dd855fee906f","resolution":{"observed_at":"2026-08-16T11:08:56.458156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:56.307141Z","title":"Multi-scale deformable transformer for multi-contrast knee MRI super-resolution,","venue":null,"work_id":"7cbf6037-a268-4a65-9318-f069e517a668","year":2023},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.061138Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:595f3ccf5176b5e4cc7b0b7639e6dd19cf169fb333d4a66cc563c09447f214f7","observation_id":"b4136cd6-fabf-439b-84ac-10b119685082","resolution":{"observed_at":"2026-08-16T11:08:56.376227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:56.204505Z","title":"Deep face super- resolution with iterative collaboration between attentive recovery and landmark estimation,","venue":null,"work_id":"79ce3aee-d340-471f-91ae-70507aff7703","year":2020},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.130984Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:045e19ad637cbed2bdb1ed641be9d5fdfae9a57218915005f5ec994423d85b3c","observation_id":"3beed210-2fcc-450b-89c5-b1be40a8acfa","resolution":{"observed_at":"2026-08-16T11:08:56.295923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:56.168662Z","title":"Progressive semantic-aware style transformation for blind face restoration,","venue":null,"work_id":"33ffbee8-8631-41be-bb8c-bbc3cc105e86","year":2021},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.165262Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:ad37cffa7c3d1aa26286f8013fe91613701a0cac207a5894d4f82b31234d1ade","observation_id":"3ffe8116-72b4-45e6-8763-581ad5c29b2a","resolution":{"observed_at":"2026-08-16T11:08:56.192429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:56.096891Z","title":"Analysis of zero-order holder discretization of two-dimensional sliding-mode control systems,","venue":null,"work_id":"65f89626-59b3-4257-9220-e420afb7d8c5","year":2008},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.264672Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:9b1fcc70d91738ad94ba7e8f1a0dbba44b85af8868daa106a3ca1bcf375af2f0","observation_id":"7b6d17e2-2716-4846-85c9-8ac7591ff0e5","resolution":{"observed_at":"2026-08-16T11:08:56.133334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04722","last_updated":"2024-01-09T18:53:20Z","snapshot_observed_at":"2026-08-17T03:22:34.203372Z","submitted_at":"2024-01-09T18:53:20Z","title":"U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04722","snapshot_observed_at":"2026-08-16T11:08:54.301257Z","title":"U-mamba: Enhancing long-range depen- dency for biomedical image segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.301257Z"},"links":{"cited_paper":"/paper/2401.04722","citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:ed19249f5f621bf1ae0662bcde3b2435c09965441cd7dde698eb5a2a9706c859","observation_id":"e1b04780-cc6b-4521-807c-1d262533427b","resolution":{"observed_at":"2026-08-16T11:08:54.301257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:55.926309Z","title":"Deform-mamba network for MRI super-resolution,","venue":null,"work_id":"4a46b528-3a33-4322-973a-2c798197a95b","year":2024},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.306558Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:7c207c8718baf37440b68c97f72ffb15385a87683ea28592cc0918bca9aa63d5","observation_id":"d8bf9449-cb2f-4ec1-9e13-4e97c4e8b216","resolution":{"observed_at":"2026-08-16T11:08:56.005183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:55.769156Z","title":"Generation of super- resolution for medical image via a self-prior guided mamba network with edge-aware constraint,","venue":null,"work_id":"6cbc4bb8-2f73-45d4-bf15-d3062f44c0a2","year":2025},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.366817Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:41633acecd1d83062effe3fbabd949e4c7ac669e457247bd208208cfe956284c","observation_id":"78e0a256-168b-42aa-9bfd-c2f0ed7efe92","resolution":{"observed_at":"2026-08-16T11:08:55.844593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:55.642587Z","title":"Squeeze-and-excitation networks,","venue":null,"work_id":"b5426388-f09b-4cf6-b9d2-57aaa52c1d0d","year":2018},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.372439Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:4186e800665548fbd6c48f69cfa026a16474f7a51922c732706fb874a9c52c93","observation_id":"940d6c91-8e78-465e-a30a-e4590f678540","resolution":{"observed_at":"2026-08-16T11:08:55.699754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:55.538402Z","title":"Loss functions for image restoration with neural networks,","venue":null,"work_id":"cd545b40-8598-4b2b-a590-36bf375045ba","year":2017},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.459809Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:f3410109b10199e89baf75e0adb3b59e180869972fdb47223cdfbdacfe9a4704","observation_id":"3b25b23b-a3c3-47c1-8317-11b394302fae","resolution":{"observed_at":"2026-08-16T11:08:55.608846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.08839","last_updated":"2019-12-11T10:31:39Z","snapshot_observed_at":"2026-08-17T20:14:40.901606Z","submitted_at":"2018-11-21T17:32:14Z","title":"fastMRI: An Open Dataset and Benchmarks for Accelerated MRI","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.08839","snapshot_observed_at":"2026-08-16T11:08:54.466342Z","title":"fastmri: An open dataset and benchmarks for accelerated mri,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.466342Z"},"links":{"cited_paper":"/paper/1811.08839","citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:e47a1a127d26d59dc69092d3a2665e5df32de4f09e1fc1037eff1a12c5677ed2","observation_id":"e1060a0d-10e0-4454-b4a9-2d11661dfb39","resolution":{"observed_at":"2026-08-16T11:08:54.466342Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:55.338880Z","title":"fastmri: A publicly available raw k-space and dicom dataset of knee images for ac- celerated mr image reconstruction using machine learning,","venue":null,"work_id":"de5f9cd4-7be4-4c83-995b-a18581222a65","year":2020},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.510009Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:9b20851edda099c9371acc6953320de68eb75757c567ae81bed1d32565bfa627","observation_id":"3453b28e-edd3-4b32-999c-4b049e1b0bd1","resolution":{"observed_at":"2026-08-16T11:08:55.433263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:55.271250Z","title":"Head and neck tumor segmentation in PET/CT: the HECKTOR challenge,","venue":null,"work_id":"4b9c2fba-eda4-457d-8609-42012b30ef40","year":2022},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.533308Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:0e6148d873ac8c04300152af9d9153e21f03bcd4775ce497fa7eaebf19fa82f7","observation_id":"5f92a4ee-c77b-4efd-a528-64343a28d488","resolution":{"observed_at":"2026-08-16T11:08:55.327542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:54.538014Z","title":"Image quality assessment: from error visibility to structural similarity,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.538014Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:ebcc837ad9a574359ba471b9ac41960bbc559d4a7a8070114444e4c3d003c518","observation_id":"c9df0745-2f88-4efb-af54-a8a208d52af5","resolution":{"observed_at":"2026-08-16T11:08:54.538014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:55.163012Z","title":"Single image super-resolution based on directional vari- ance attention network,","venue":null,"work_id":"f0f14e73-9a36-4147-8b87-a676c9a0bb13","year":2023},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.542802Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:52391c9cbaee5431fbef82836169aa0d73c435a075b314791e88a3a66580a41c","observation_id":"c50a7582-7be8-4443-ae10-84f75155dc8b","resolution":{"observed_at":"2026-08-16T11:08:55.199047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:55.051933Z","title":"Multi-contrast super-resolution MRI through a progressive network,","venue":null,"work_id":"0b822b66-3adc-4e3d-add8-bff0798f6db9","year":2020},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.569565Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:2d87995397c826f222143a1161e31469ef5c4cda2a1c50832fedf910018bb4d7","observation_id":"f7f34cac-2bb6-480b-b5c9-175d67a3d9c0","resolution":{"observed_at":"2026-08-16T11:08:55.119432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:54.943221Z","title":"Swinir: Image restoration using swin transformer,","venue":null,"work_id":"79637b98-ace8-49cf-8668-d6c25f73e45d","year":2021},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.595020Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:5d2d0218ce0fa6ec48f9781ae9c72dcf4c96e2d2db4ae14ec03a9461b695b617","observation_id":"e86323f5-ac9f-420f-b382-ce01059693b0","resolution":{"observed_at":"2026-08-16T11:08:55.040295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:08:54.792195Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows,","venue":null,"work_id":"b8593ff3-15ad-472b-98c5-cd833f0caeb4","year":2021},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.624746Z"},"links":{"citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:e31ec34cf97e3a69e73e7ff5a2710428118c920df76a5d167d5d349a4c217cab","observation_id":"0b1357c1-6e62-431b-9e7f-bd33821cf3c8","resolution":{"observed_at":"2026-08-16T11:08:54.830350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.03999","last_updated":"2018-05-20T23:33:30Z","snapshot_observed_at":"2026-08-19T16:10:23.498341Z","submitted_at":"2018-04-11T14:13:03Z","title":"Attention U-Net: Learning Where to Look for the Pancreas","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.03999","snapshot_observed_at":"2026-08-16T11:08:54.629908Z","title":"Attention u-net: Learning where to look for the pancreas,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:54.629908Z"},"links":{"cited_paper":"/paper/1804.03999","citing_paper":"/paper/2504.16958"},"observation_digest":"sha256:7ac8eb61e97fbdce97e0b3592687abd48d7cceb96b68f2794c0619fa5bf082e6","observation_id":"7b7f2e10-4e7c-4c87-861a-e51efc6e59f1","resolution":{"observed_at":"2026-08-16T11:08:54.629908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2504.16958","last_updated":"2025-04-23T03:25:31Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-17T21:12:10.875946Z","submitted_at":"2025-04-23T03:25:31Z","title":"Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":43},"total_outbound_references":53},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2504.16958."}