{"as_of":"2026-08-22T08:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b2cffb12c5596c48d03c316c3fd00c291ed740d192049799bf960f9700595c2d","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:26:49.122833Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T22:44:24.834974Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-15T22:44:25.143857Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"cited_work":{"arxiv_id":"2501.05091","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.05091","snapshot_observed_at":"2026-08-15T22:44:25.143857Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","venue":"cs.CV","work_id":"7f887214-1924-44c3-8053-2580689227c8","year":2025},"citing_paper":{"arxiv_id":"2505.06576","last_updated":"2025-05-16T10:39:40Z","snapshot_observed_at":"2026-08-15T22:36:34.211500Z","submitted_at":"2025-05-10T09:26:22Z","title":"Two-Stage Random Alternation Framework for One-Shot Pansharpening","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T22:44:24.834974Z"},"links":{"cited_paper":"/paper/2501.05091","citing_paper":"/paper/2505.06576"},"observation_digest":"sha256:93e62f8f135a46a0b4ddaecfcdf0ec7345ca0a07bf07d562afeb8fb3fdf25281","observation_id":"87af3442-67fd-4871-9c36-4fd997d6fcb9","resolution":{"observed_at":"2026-08-15T22:44:25.148521Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.05091/citation-record","integrity":"/paper/2501.05091/integrity","json":"/paper/2501.05091/citation-record.json","paper":"/paper/2501.05091"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:26:49.878748Z","title":"Extracting spectral contrast in landsat thematic mapper image data using selective principal component analysis,","venue":null,"work_id":"afb84590-d532-41f7-8d12-41d7577c67fc","year":1989},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.867741Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:ddb7d8e4c7c5576f7639035d1b0ef458b34978250f913b8b13d713887a8daace","observation_id":"8ef34ef0-f987-4629-8d4c-ee1f4f45e839","resolution":{"observed_at":"2026-08-10T21:26:49.884027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:48.873830Z","title":"Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.873830Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:8197a0419b3a1949af1d4d9795ac68c8aa747da1e0ed509cfd5e8620851d70ac","observation_id":"47d07e18-38af-48a2-9472-6d3b10ab628c","resolution":{"observed_at":"2026-08-10T21:26:48.873830Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:26:48.878974Z","title":"Improving component substitution pansharpening through multivariate regression of ms + pan data,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.878974Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:b76f87224ba15f1dd81837b39d85e546d376f199d0f36df6a296f4aec3b6fdde","observation_id":"f34867fb-d7dc-4965-b07c-5e5ecdebf0f3","resolution":{"observed_at":"2026-08-10T21:26:48.878974Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:26:48.885654Z","title":"Hyperspectral pansharpening with guided filter,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.885654Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:f91fb42f08fce1c0e4485dd1cdb93728cc9eac55c63a7ea2318c3f5299f7f2ab","observation_id":"b6fa79fd-bf52-408a-b154-88891fa73795","resolution":{"observed_at":"2026-08-10T21:26:48.885654Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:26:48.890600Z","title":"Introduction of sensor spectral response into image fusion methods. application to wavelet-based methods,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.890600Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:adacc20a6a1f59c019407f26cec462130f76912035057864af3f0053e1c87360","observation_id":"28255d2e-517d-4815-a813-ed42b518daf5","resolution":{"observed_at":"2026-08-10T21:26:48.890600Z","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":"2026-08-10T21:26:49.826775Z","title":"Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details,","venue":null,"work_id":"9ff0f49b-e448-4e58-8869-557c78f042e5","year":2000},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.896230Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:c0181f8e09b4da57de093cee55d0c33ee6169dbad7f527215479e40fd8ed2f67","observation_id":"9f071866-e87e-448f-98f5-3a8a999c3814","resolution":{"observed_at":"2026-08-10T21:26:49.831991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.811168Z","title":"MTF- tailored multiscale fusion of high-resolution ms and pan imagery,","venue":null,"work_id":"b9a94fc0-948c-488e-addd-e03ea81f43fd","year":2006},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.901745Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:a631c543f142f399b6c92b1f1595052d495454db85c28ed30ed9a4e8168659ba","observation_id":"e109a7f7-b2bc-4188-b38a-2a93c3e025d6","resolution":{"observed_at":"2026-08-10T21:26:49.816075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.795667Z","title":"Contrast and error-based fusion schemes for multispectral image pansharpening,","venue":null,"work_id":"4c54da75-8eb7-446b-83cc-fbd3351ee13a","year":2013},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.906966Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:d84404d70de0d076a1fb28fe9220674cd3b74432e401f310aecebc51c3f79454","observation_id":"06a6c194-6575-4da4-b707-4c923ab7400f","resolution":{"observed_at":"2026-08-10T21:26:49.800575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.779314Z","title":"A new pansharpening method based on spatial and spectral sparsity priors,","venue":null,"work_id":"0d0b5f6a-5d87-4484-aea5-396c7fc1af95","year":2014},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.912043Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:0b9313dc18005cac32a7d46583b86825a7547e94c859e6b929c53fc3220873e7","observation_id":"497dd3d9-c899-4295-9a49-ec272d1c2c37","resolution":{"observed_at":"2026-08-10T21:26:49.784635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:48.917064Z","title":"A variational approach to hyperspectral image fusion,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.917064Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:78ffec02d6121f2ddcf330f3a9922eec7fa3425c647ad39445f42bc9244dc186","observation_id":"af2d1bc4-6243-412f-8a62-c0286a45183f","resolution":{"observed_at":"2026-08-10T21:26:48.917064Z","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":"2026-08-10T21:26:49.752518Z","title":"High-quality bayesian pan- sharpening,","venue":null,"work_id":"43cbd858-c57a-4531-95f4-28046d32124e","year":2018},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.921875Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:5d05345b5799fbc761fc3c20535bae79d2c3207c3700940baeb404405e9b4879","observation_id":"447cfd54-69d5-48db-8889-a2ca3dd4d9e1","resolution":{"observed_at":"2026-08-10T21:26:49.758111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.736635Z","title":"Lrtcfpan: Low-rank tensor completion based framework for pansharpening,","venue":null,"work_id":"89b9c0e6-2cb2-4f88-ae08-9ae9113f3a09","year":2023},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.927440Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:6920faffd475b030483d110212cf80b96f4850bd55f870fe55d8ce4b55d7841b","observation_id":"0fc8501b-6242-4c05-8dc2-9783451f6436","resolution":{"observed_at":"2026-08-10T21:26:49.741327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.721410Z","title":"Pansharpening by convolutional neural networks,","venue":null,"work_id":"de9aa157-01b4-4873-a287-3d553796405e","year":2016},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.932338Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:f84b7b5ce7178748f028e781de6bc5e472b406b845d19ace40197ef7a8748b90","observation_id":"009e83ee-1026-41f6-be93-2dd9827b8277","resolution":{"observed_at":"2026-08-10T21:26:49.726414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.705144Z","title":"PanNet: A deep network architecture for pan-sharpening,","venue":null,"work_id":"72cbb18b-b3b1-4ad0-9d59-ec7dec87db81","year":2017},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.937400Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:5e75bb25a9979d5692e268ed132ac864e5c04d45dfb5f74852fda94761d039dc","observation_id":"773565b1-df35-411d-b9d4-80a5ede3a023","resolution":{"observed_at":"2026-08-10T21:26:49.710408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.689211Z","title":"Detail injection- based deep convolutional neural networks for pansharpening,","venue":null,"work_id":"fececb57-f097-4ad3-8ac4-934b6a9574c5","year":2020},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.941955Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:ee02a8ee2a855efb69d39fbb003174e674a76d6055133b1d638d197ce34338d6","observation_id":"fb51aeb1-9e37-4c80-a6c9-c5bb9bd46519","resolution":{"observed_at":"2026-08-10T21:26:49.694573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.672667Z","title":"Model- informed multistage unsupervised network for hyperspectral image super-resolution,","venue":null,"work_id":"9f968067-0029-445c-87ec-8636e8b067d4","year":2024},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.946434Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:01891b8796c9884dde3d96c386bd88b79fdad6f58c72fe249ef8656bd3e94cf6","observation_id":"311a621b-257a-40a2-bc99-49ef1950868d","resolution":{"observed_at":"2026-08-10T21:26:49.677894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:48.950872Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.950872Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:53008cbc01daf0a52555a26b60148097bbae8af7a754dcb6c131a68c53561ae9","observation_id":"159eea17-4a3f-487f-b484-90d1132ca585","resolution":{"observed_at":"2026-08-10T21:26:48.950872Z","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":"2026-08-10T21:26:49.647881Z","title":"U-net: Convolutional networks for biomedical image segmentation,","venue":null,"work_id":"f8872d4d-3b2c-457e-a5dd-602153015fa9","year":2015},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.955353Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:03bcad8eaef9573abc7ac0b05b9a82317812175a0df3ae3a0b9786718026cb84","observation_id":"81e5387c-3c1f-490e-944d-51df9e655956","resolution":{"observed_at":"2026-08-10T21:26:49.652722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.632580Z","title":"Remote sensing image fusion based on two-stream fusion network,","venue":null,"work_id":"beca484a-3865-4e51-8138-969a250efbc2","year":2020},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.959967Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:b7fe2c9336d17602a75f4dedcea76df59ece8581c72eaf7998da77a76b782fed","observation_id":"6674418a-e74a-4aa9-b2b1-d46443677bd6","resolution":{"observed_at":"2026-08-10T21:26:49.637493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:48.964652Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.964652Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:92d12a778ba9002249335a26398b036fb739d7afe8794cbe642c6e5fd9aa906c","observation_id":"17ec428d-27c2-405f-8cfb-528e0892d042","resolution":{"observed_at":"2026-08-10T21:26:48.964652Z","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":"2026-08-10T21:26:49.605033Z","title":"Elucidating the design space of diffusion-based generative models,","venue":null,"work_id":"f36ecf2d-5b7f-4cb4-adb2-6ed886c73fd9","year":2022},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.969297Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:d9ff0cca7c56d38c0780dd1f9992da8a44676e54dcf76d6483809ba2b6b3dfbe","observation_id":"f01a8efa-d0ea-4153-9e24-d5c77f6bbb27","resolution":{"observed_at":"2026-08-10T21:26:49.610231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.589709Z","title":"Palette: Image-to-image diffusion models,","venue":null,"work_id":"3712f46c-554c-44ee-80c8-7a07f63c8db0","year":2022},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.973978Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:9b9e8f35f1835394623908d82d88ba5dde0531204978dcbba0c09fe45ebd8720","observation_id":"2615150a-30d8-433f-8a88-2aaed76d5e6b","resolution":{"observed_at":"2026-08-10T21:26:49.594893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07636","last_updated":"2021-06-30T07:34:57Z","snapshot_observed_at":"2026-08-16T18:31:18.345286Z","submitted_at":"2021-04-15T17:50:42Z","title":"Image Super-Resolution via Iterative Refinement","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.07636","snapshot_observed_at":"2026-08-10T21:26:48.978608Z","title":"Image Super-Resolution via Iterative Refinement,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.978608Z"},"links":{"cited_paper":"/paper/2104.07636","citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:fc875df448cae6205b016997f840b2dc9d9b432754f5c692a21e697aceeb55b8","observation_id":"14097501-2ad1-4202-862f-3ece064f121a","resolution":{"observed_at":"2026-08-10T21:26:48.978608Z","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":"2026-08-10T21:26:49.574780Z","title":"Resshift: Efficient diffusion model for image super-resolution by residual shifting,","venue":null,"work_id":"966ed539-a536-428d-850a-aaae47a50f06","year":2024},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.983855Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:264ca06906d3913813282a486b526c102c042619a5a09ae71e18872a2b7f923a","observation_id":"b12a2863-a6eb-4c33-a2f2-7ac1a48c22b0","resolution":{"observed_at":"2026-08-10T21:26:49.579915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.557959Z","title":"Pandiff: A novel pansharpening method based on denoising diffusion probabilistic model,","venue":null,"work_id":"14a0577b-f344-4e1e-bbb8-69c3c2234899","year":2023},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.988718Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:cfe127048ec7cf44396521cf40ad7a552cc3e2de5684e6449182b7056e10dceb","observation_id":"466646aa-ab4a-44c7-9fe5-703c3de06c7c","resolution":{"observed_at":"2026-08-10T21:26:49.562998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.541239Z","title":"Diffusion model with disentangled modulations for sharpening multispectral and hyperspectral images,","venue":null,"work_id":"7394059a-a8bf-4bc9-9040-cc96f835fca8","year":2024},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.993754Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:3380da5c7e346d06dfdc65047a1d0ab5a2b8e1cc7e70d9e29f304e3dec9295f3","observation_id":"1812ad9d-cf8c-4978-92c6-1f800d2bdc1d","resolution":{"observed_at":"2026-08-10T21:26:49.546394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:48.999243Z","title":"Denoising diffusion implicit models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:48.999243Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:ab40009f2d943f7212318b3b73a69292b5de0d00eed54aa05e68a48944fc440e","observation_id":"8264647d-a83c-4332-a614-ef821419ee17","resolution":{"observed_at":"2026-08-10T21:26:48.999243Z","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":"2026-08-10T21:26:49.515858Z","title":"Improved denoising diffusion probabilis- tic models,","venue":null,"work_id":"dbaedab3-2a19-438b-88da-654a81771b02","year":2021},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.004862Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:ab6170cd2cd1273915fe8a41a35598d440897c7e70dc4fbe8f7c616953c6fea0","observation_id":"216925d2-c932-4855-baf8-f4130dca7916","resolution":{"observed_at":"2026-08-10T21:26:49.521120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.499976Z","title":"Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,","venue":null,"work_id":"2913a04c-a49e-42c3-8339-fb4fd22850db","year":2022},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.010444Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:eb2005190bf665337ce545e7cfcfcc26a0a3251c65734d9e308f8430f30d79eb","observation_id":"9c0ba669-2a0a-400b-b956-f1a4ac582f18","resolution":{"observed_at":"2026-08-10T21:26:49.505330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.08500","last_updated":"2018-01-12T14:05:44Z","snapshot_observed_at":"2026-08-22T05:12:22.477450Z","submitted_at":"2017-06-26T17:45:23Z","title":"GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.08500","snapshot_observed_at":"2026-08-10T21:26:49.015256Z","title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.015256Z"},"links":{"cited_paper":"/paper/1706.08500","citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:47d0dd3eab2dedbcf7e6e56db960468d8493b477c757091e174974e57283bb3b","observation_id":"d1d51f09-e8cc-4d12-9c3f-34ae86f47942","resolution":{"observed_at":"2026-08-10T21:26:49.015256Z","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":"2026-08-10T21:26:49.484027Z","title":"Daubechies, Ten lectures on wavelets","venue":null,"work_id":"4956cb69-544b-4bbf-90b1-d197a8da1c6e","year":1992},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.020475Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:6c19fdc12fa9f7b181d3973665c7a23bb3c9e4716a2a0c6fa5eb23e421202d80","observation_id":"cb522103-e578-4bde-98cd-659bde565f6e","resolution":{"observed_at":"2026-08-10T21:26:49.489144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.025567Z","title":"Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.025567Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:b50405e4e68c6e62992bc50b2c514808c911f291946dd33b7ba88a64edf81999","observation_id":"e3c8caa3-a787-4501-8436-0f412cfd9980","resolution":{"observed_at":"2026-08-10T21:26:49.025567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.03003","last_updated":"2022-09-07T08:59:55Z","snapshot_observed_at":"2026-07-06T13:49:40.974495Z","submitted_at":"2022-09-07T08:59:55Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.03003","snapshot_observed_at":"2026-08-10T21:26:49.030691Z","title":"Flow straight and fast: Learning to generate and transfer data with rectified flow,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.030691Z"},"links":{"cited_paper":"/paper/2209.03003","citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:fc48a1de3254170211ff7b764d3591c9f983cf4db362c9c38785aac8588ecdaf","observation_id":"baea46b2-c244-4060-841d-30c03157ad7c","resolution":{"observed_at":"2026-08-10T21:26:49.030691Z","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":"2026-08-10T21:26:49.455912Z","title":"Residual de- noising diffusion models,","venue":null,"work_id":"bb2f045b-0649-4243-9d81-04baa8e2251a","year":2024},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.037264Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:7cc66ac4a869e6f5031b12ef3f664ad0664f29d5fd061e2af14dc6f9a886c12d","observation_id":"2a385bfc-eac9-4500-bd4e-016c7d0e6fb7","resolution":{"observed_at":"2026-08-10T21:26:49.460832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.440235Z","title":"Discrimination among semi-arid landscape endmembers using the spectral angle mapper (sam) algorithm,","venue":null,"work_id":"99b46de2-ff9c-489d-abc3-1826a083c834","year":1992},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.042174Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:66c41a663770e951f39adff688abd20af633aa813b9618782687529dc3684369","observation_id":"e1c478f3-81db-48dc-a3ae-19c987efda51","resolution":{"observed_at":"2026-08-10T21:26:49.445374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.424811Z","title":"Wald, Data fusion: definitions and architectures: fusion of images of different spatial resolutions","venue":null,"work_id":"4f224134-5b45-4fde-877f-3aaa993e1003","year":2002},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.047460Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:e11b78bd5677f07860883456f2f5904c350305e401891eed2e8fff24cf254384","observation_id":"2ce447e6-cbc9-4f49-9d3a-f39f3061d77d","resolution":{"observed_at":"2026-08-10T21:26:49.429810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.407533Z","title":"Hypercomplex quality assessment of multi/hyperspectral images,","venue":null,"work_id":"faf0ddf1-53d5-4b70-97d0-c9ba2c68259d","year":2009},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.052769Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:11929acb31d0746f05f7c29fc57caace571a4e73763c0463f0be81d157bf1e59","observation_id":"703214f2-2790-45ba-9f97-269bed990b71","resolution":{"observed_at":"2026-08-10T21:26:49.413985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.390836Z","title":"A wavelet transform method to merge landsat tm and spot panchromatic data,","venue":null,"work_id":"803b42e9-d9bc-42d0-af77-a0691854b341","year":1998},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.057842Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:8da54050781394bbf416c0a3180db4cec4b7c865febe137aa4cd42a5b6574d06","observation_id":"8c5fd7e9-ad8e-4892-b3aa-eb7e61953e60","resolution":{"observed_at":"2026-08-10T21:26:49.396624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.063220Z","title":"A critical comparison among pansharpening algorithms,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.063220Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:5289f04fc7feb4786bbd7a61b0608bf78a18d550981f1c9f5cbedb0930d0f3b2","observation_id":"88401f1e-3a33-4d06-8506-7d6a7186a4d2","resolution":{"observed_at":"2026-08-10T21:26:49.063220Z","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":"2026-08-10T21:26:49.365547Z","title":"Context-driven fusion of high spatial and spectral resolution images based on oversam- pled multiresolution analysis,","venue":null,"work_id":"1a45d77b-5fa1-4b21-9ce8-74d282c1c9a9","year":2002},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.067885Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:90b632698da41314a5d96b99518d3f27859fe8e2edaf71ead1101f1440abdb0a","observation_id":"86fbee5c-c561-48d1-9dbc-05d9d95f6fbd","resolution":{"observed_at":"2026-08-10T21:26:49.370661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.072391Z","title":"Robust band-dependent spatial-detail approaches for panchromatic sharpening,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.072391Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:a1d6f12979d2092586a7a3555bbac614cf4dcae47e203172d2f32e3dde8e6683","observation_id":"38e4a153-c47e-4fa4-b23a-06c25c7559a1","resolution":{"observed_at":"2026-08-10T21:26:49.072391Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:26:49.077168Z","title":"Full scale regression-based injection coefficients for panchromatic sharpening,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.077168Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:39b3c2bc32029b5c32081cef5a21112a40e17167d25fbe986332dc8f1859d873","observation_id":"e1731ccf-0412-4740-b6d7-a7b09a93d8af","resolution":{"observed_at":"2026-08-10T21:26:49.077168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:26:49.082276Z","title":"Haze correction for contrast-based multispectral pansharpening,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.082276Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:bc943178a120b4cea67858ebc8e01b26579255c752be14a2c7bee44758b5b066","observation_id":"e28d9e30-b3fe-48a3-99fd-c8313102537e","resolution":{"observed_at":"2026-08-10T21:26:49.082276Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:26:49.087545Z","title":"Pansharp- ening via detail injection based convolutional neural networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.087545Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:52ac5332c5b861fcdfb98cd0b5812adddf5ac92778bfd39743f4fa925afce7ed","observation_id":"72796116-fb68-4090-8b25-020ab8dd2480","resolution":{"observed_at":"2026-08-10T21:26:49.087545Z","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":"2026-08-10T21:26:49.306819Z","title":"A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening,","venue":null,"work_id":"e3d5bb1a-a959-424f-84ee-394b242f5d79","year":2018},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.092433Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:7cff3d30cf3e6bb17b04270d4a3bcbdb3adb8bcc4784b5ce8f3e6ac671a5b6f3","observation_id":"45ff57de-aa8b-4f02-b159-141386e8bd0a","resolution":{"observed_at":"2026-08-10T21:26:49.312661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.288201Z","title":"Zero-shot semi- supervised learning for pansharpening,","venue":null,"work_id":"69051086-96c9-401a-9223-81575f969abc","year":2024},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.097352Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:235871ad507a8b5b0f5a9f0c914e458911a4f397013746d83e209c95e9a9f668","observation_id":"abd88815-adf1-4e1d-86c7-3598e21d2fe6","resolution":{"observed_at":"2026-08-10T21:26:49.294156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.271334Z","title":"U2net: A general frame- work with spatial-spectral-integrated double u-net for image fusion,","venue":null,"work_id":"80a30d51-f327-4643-9726-970dc67fffc8","year":2023},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.103438Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:318834d9f318c3a0bb8c2249958d223ba53b450ee30e1f36965d813d15e7fb55","observation_id":"b8b0759c-55f7-44dc-9f1d-68e0a0862d93","resolution":{"observed_at":"2026-08-10T21:26:49.276663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.255801Z","title":"A general paradigm with detail-preserving conditional invertible network for image fusion,","venue":null,"work_id":"55717d16-42a7-4d46-9b4e-d8bd358fd759","year":2024},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.108472Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:36e2d522453350e135b5fd034c7a48bcfa1e16da9059c89fa634623ece17b6d3","observation_id":"7227fc41-7b8a-4594-9aee-d970b590bd3e","resolution":{"observed_at":"2026-08-10T21:26:49.260813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.239911Z","title":"Pan-sharpening with customized transformer and invertible neural network,","venue":null,"work_id":"c35a17f6-56df-4877-b198-5f47aaefebb1","year":2022},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.113369Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:fbedaa2673e733b8852b79155a2b1af5e35f0f288af49fb6fc561e3a377ebef0","observation_id":"946c1087-61dd-4d2a-9efc-d0998dec290d","resolution":{"observed_at":"2026-08-10T21:26:49.245003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.223657Z","title":"Memory-augmented model- driven network for pansharpening,","venue":null,"work_id":"896d0318-50f0-49c7-af45-91ec581f6236","year":2022},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.118031Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:fe0bfcaed00cf8647d20d530716aa80006c27a65b867051ede6e954f9a7eb195","observation_id":"f3dc3648-f365-467b-8115-214229cac15b","resolution":{"observed_at":"2026-08-10T21:26:49.228819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T21:26:49.205836Z","title":"Dynamic cross feature fusion for remote sensing pansharpening,","venue":null,"work_id":"1b552f5e-6529-40c4-8228-8b4d4c206c37","year":2021},"citing_paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:49.122833Z"},"links":{"citing_paper":"/paper/2501.05091"},"observation_digest":"sha256:c9fd0a841050bbee8a34722f6eb8ca6d9a11baf0449ce665e892cbcbfcd2c0b7","observation_id":"16a93570-dacb-4201-9929-3b9af0678fc1","resolution":{"observed_at":"2026-08-10T21:26:49.212798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.05091","last_updated":"2025-01-10T08:43:50Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-20T07:35:37.349517Z","submitted_at":"2025-01-09T09:15:07Z","title":"ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":0,"verified_fuzzy":34},"total_outbound_references":51},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2501.05091."}