Paper Citation Record · LEDGER

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising

As of 14 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2509.05662.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.05662 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:18:19.830383Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

14 of 14 outbound references displayed

verified exact3
verified fuzzy9
unresolved2
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation faac9367-0376-4688-829c-cf1eb9cb34d2 · outbound

This paper cites Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising,.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:18:19.980095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8eebd29d-eac4-4749-8752-e08a0a140078 · outbound

This paper cites FFDNet: Toward a Fast and Flexible Solution for CNN- based Image Denoising,.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising FFDNet: Toward a Fast and Flexible Solution for CNN- based Image Denoising,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:18:19.971986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 79e45229-8dfc-41ec-bf07-29348b3640b7 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation,.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising U-Net: Convolutional Networks for Biomedical Image Segmentation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:18:19.963480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6930d9f1-b387-4002-9942-4e0ee28ee7b2 · outbound

This paper cites Restormer: Efficient Transformer for High-Resolution Image Restoration,.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising Restormer: Efficient Transformer for High-Resolution Image Restoration,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:18:19.955144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fef67716-283c-4ea4-b592-a0fbde810cf2 · outbound

This paper cites Performance of pile-up mitigation techniques for jets in pp col- lisions at √s= 8 TeV using the ATLAS detector,.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising Performance of pile-up mitigation techniques for jets in pp col- lisions at √s= 8 TeV using the ATLAS detector,

Reference 5

Resolution: verified exact; doi, observed 2026-08-05T05:18:19.888602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7362e6e8-e646-4e9a-9758-9bd5b36c1096 · outbound

This paper cites Pileup and Underlying Event Mitigation with Iterative Constituent Subtraction,.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising Pileup and Underlying Event Mitigation with Iterative Constituent Subtraction,

Reference 6

Resolution: verified exact; doi, observed 2026-08-05T05:18:19.877960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation aed9c200-1cce-48d2-9cc4-d1239a943121 · outbound

This paper cites Pileup mitigation at CMS in 13 TeV data,.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising Pileup mitigation at CMS in 13 TeV data,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T05:18:19.809270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21bf56b0-56a9-4fc0-a5b3-8b3d89f59bfd · outbound

This paper cites Pileup Per Particle Identification,.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising Pileup Per Particle Identification,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T05:18:19.812188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd6725c1-438a-4efd-9928-7a5c604b929f · outbound

This paper cites Pileup mitigation at the Large Hadron Collider with Graph Neural Networks.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising Pileup mitigation at the Large Hadron Collider with Graph Neural Networks

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-05T05:18:19.901002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a8fe2455-8fc1-448a-9cf2-0383d1893c31 · outbound

This paper cites Learning Multiple Layers of Features from Tiny Images.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising Learning Multiple Layers of Features from Tiny Images

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:18:19.946351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 51ad3371-211c-4430-b88e-412db1a4a250 · outbound

This paper cites A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics,.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:18:19.937680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0042b5fe-3525-4e2f-ab2f-3acddba69b38 · outbound

This paper cites Robust Image Denoising through Adversarial Frequency Mixup,.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising Robust Image Denoising through Adversarial Frequency Mixup,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:18:19.928683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e1639e6a-eca1-4cba-aa74-cc3c18c2e18e · outbound

This paper cites LAN: Learning to Adapt Noise for Image Denoising,.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising LAN: Learning to Adapt Noise for Image Denoising,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:18:19.919974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 450d036c-ac56-473b-b412-a60e702deb86 · outbound

This paper cites DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables,.

WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:18:19.910862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.