Paper Citation Record · LEDGER

Residual Transformer Fusion Network for Salt and Pepper Image Denoising

As of 8 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 1 inbound Pith citation observation for arXiv:2502.09000.

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

pith.paper-citation-record.v1
2502.09000 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:01:17.130905Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T10:54:39.545990Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

verified exact1
verified fuzzy4
unresolved9
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 728e0591-e17e-4ff8-a37f-8d59a7ee7a8d · outbound

This paper cites Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T23:01:17.073971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4cf34aa-dd5f-4052-b70d-4d3ebdb3c4ab · outbound

This paper cites Unsupervised representation learning with deep convolutional generative adversarial networks,.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising Unsupervised representation learning with deep convolutional generative adversarial networks,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T23:01:17.087480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0896415-3532-4fd3-9fe8-35c7121c8c3a · outbound

This paper cites Attention Is All You Need.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising Attention Is All You Need

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T23:01:17.094346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16d893db-6e8f-476b-ad51-41050547be76 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T23:01:17.098736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e529189-274c-41c4-9b48-e5ace37efa68 · outbound

This paper cites CvT: Introducing Convolutions to Vision Transformers.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising CvT: Introducing Convolutions to Vision Transformers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T23:01:17.102588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 242d62ac-603d-4983-a9a9-3477177c1c53 · outbound

This paper cites Awgn-based image denoiser using convolutional vision transformer,.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising Awgn-based image denoiser using convolutional vision transformer,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:01:17.251335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4b432e9e-de66-4397-9571-ae9434a6cb99 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising Deep Residual Learning for Image Recognition

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T23:01:17.109767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5373524-c834-4fde-b5f1-9020ed6ed9a1 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Residual Transformer Fusion Network for Salt and Pepper Image Denoising Gaussian Error Linear Units (GELUs)

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T23:01:17.113762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72e5c65d-f0ca-420f-983d-6c022044a106 · outbound

This paper cites A new fast and efficient decision-based algorithm for removal of high-density impulse noises,.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising A new fast and efficient decision-based algorithm for removal of high-density impulse noises,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:01:17.243339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9780d9d4-41ca-4c98-803d-204889d39691 · outbound

This paper cites Adaptive switching non-local filter for the restoration of salt and pepper impulse-corrupted digital images,.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising Adaptive switching non-local filter for the restoration of salt and pepper impulse-corrupted digital images,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:01:17.234585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation adbf8a54-4b81-4f09-846b-40321514edda · outbound

This paper cites PARIGI: a Patch-based Approach to Remove Impulse-Gaussian Noise from Images,.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising PARIGI: a Patch-based Approach to Remove Impulse-Gaussian Noise from Images,

Reference 12

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T23:01:17.223982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f8b90e7c-efd9-4338-a248-5c23dc114474 · outbound

This paper cites A Convolutional Neural Networks Denoising Approach for Salt and Pepper Noise.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising A Convolutional Neural Networks Denoising Approach for Salt and Pepper Noise

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-07T23:01:17.159793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 556945ec-98ae-44d4-9861-1d1fb391d387 · outbound

This paper cites Image denoising: Can plain neural networks compete with bm3d?.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising Image denoising: Can plain neural networks compete with bm3d?

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:01:17.214779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 16f06891-85b5-4c56-be82-1c33bca4b9fe · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T23:01:17.090962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4fbfd12-2cc4-433d-b5a1-6988dd59980c · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Residual Transformer Fusion Network for Salt and Pepper Image Denoising MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T23:01:17.083289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 9461b462-d7a3-4008-8fb8-761b33593af0 · inbound

FUME: Fused Unified Multi-Gas Emission Network for Livestock Rumen Acidosis Detection cites this paper.

FUME: Fused Unified Multi-Gas Emission Network for Livestock Rumen Acidosis Detection Residual Transformer Fusion Network for Salt and Pepper Image Denoising

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T10:54:39.545990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.