Paper Citation Record · LEDGER

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration

As of 13 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 4 inbound Pith citation observations for arXiv:2412.03814.

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

pith.paper-citation-record.v1
2412.03814 v2

Coverage vector

measured 69 of 69 reference resolution

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measured 73 of 73 standing notices

One-hop event checks from named stored sources.

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measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T06:31:30.955626Z

Reference resolution

69 of 69 outbound references displayed

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External citation measurements

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Outbound references

Observation 1e1d8484-6527-48c0-a620-aad5933f663c · outbound

This paper cites Fast, accurate, and lightweight super-resolution with cascading residual network.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Fast, accurate, and lightweight super-resolution with cascading residual network

Reference 1

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Observation 21827713-c1ff-4e46-a03e-9ff0e01a4042 · outbound

This paper cites Contour detection and hierarchical image segmen- tation.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Contour detection and hierarchical image segmen- tation

Reference 2

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Observation 9fabb0b3-ff69-45d3-9a76-b774ed6f1f96 · outbound

This paper cites Low-complexity single-image super-resolution based on nonnegative neighbor embedding.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Low-complexity single-image super-resolution based on nonnegative neighbor embedding

Reference 3

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Observation bb2b6f6d-99bc-4633-9b0d-78909f0b083b · outbound

This paper cites End- to-end object detection with transformers.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration End- to-end object detection with transformers

Reference 4

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Observation f107e472-81a0-4522-8db6-eeebcc1518ac · outbound

This paper cites Cas-cnn: A deep convolutional neural network for image compression artifact suppression.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Cas-cnn: A deep convolutional neural network for image compression artifact suppression

Reference 5

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Observation f1944fca-20fb-47c0-9e9b-cc447fffb516 · outbound

This paper cites Pre-trained image processing transformer.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Pre-trained image processing transformer

Reference 6

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Observation 33de3adf-f3e7-47e2-8ca8-2eed307c9902 · outbound

This paper cites Activating more pixels in image super-resolution transformer.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Activating more pixels in image super-resolution transformer

Reference 7

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Observation 7d022522-1e88-4696-be68-5a22ba1f4cbe · outbound

This paper cites Better” cmos” produces clearer images: Learning space-variant blur estimation for blind image super-resolution.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Better” cmos” produces clearer images: Learning space-variant blur estimation for blind image super-resolution

Reference 8

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Observation 21c3ca3b-d1b5-4564-993a-3e604aab4e28 · outbound

This paper cites Recursive Generalization Transformer for Image Super-Resolution.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Recursive Generalization Transformer for Image Super-Resolution

Reference 9

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Observation 77ce52c3-81a9-4473-bf80-51b42409ef10 · outbound

This paper cites Dual aggregation transformer for image super-resolution.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Dual aggregation transformer for image super-resolution

Reference 10

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Observation e3f63095-db8c-4121-acf4-c5e0a6b2c336 · outbound

This paper cites Second-order attention network for single image super-resolution.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Second-order attention network for single image super-resolution

Reference 11

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Observation bada3f7c-e570-4768-bc66-d56b50d366fc · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Imagenet: A large-scale hierarchical image database

Reference 12

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Observation c0a3a875-8f31-4544-852e-94cd8d48fec0 · outbound

This paper cites Learning a deep convolutional network for image super- resolution.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Learning a deep convolutional network for image super- resolution

Reference 13

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Observation ef7958b5-e7d4-4d39-9447-cebb85479786 · outbound

This paper cites Compression artifacts reduction by a deep convolu- tional network.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Compression artifacts reduction by a deep convolu- tional network

Reference 14

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Observation 81a4b3f6-e161-4dc9-8ad7-9fc3a55dc74c · outbound

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

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 15

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Observation 8667427d-580d-4b12-8a6c-0a6cbf2265b1 · outbound

This paper cites Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures

Reference 16

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Observation 6c6795b4-eb71-45de-9a71-8de43bd0f747 · outbound

This paper cites Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models

Reference 17

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Observation 7311d815-7942-4664-86e7-a9fcb9d945cc · outbound

This paper cites Jpeg artifacts reduction via deep convolutional sparse coding.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Jpeg artifacts reduction via deep convolutional sparse coding

Reference 18

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Observation a75f0dc5-36e0-47d0-82f2-ec6c59f16468 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 19

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This paper cites RWKV-CLIP: A Robust Vision-Language Representation Learner.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration RWKV-CLIP: A Robust Vision-Language Representation Learner

Reference 20

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Observation d085741c-9126-4836-8537-ec3fed5b89ec · outbound

This paper cites MambaIR: A Simple Baseline for Image Restoration with State-Space Model.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration MambaIR: A Simple Baseline for Image Restoration with State-Space Model

Reference 21

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Observation b2d7329d-8e79-45e7-8365-fccad0b44727 · outbound

This paper cites PointRWKV: Efficient RWKV-Like Model for Hierarchical Point Cloud Learning.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration PointRWKV: Efficient RWKV-Like Model for Hierarchical Point Cloud Learning

Reference 22

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Observation 04eba086-7071-4b26-92ed-6f99f1c57c0f · outbound

This paper cites Single image super-resolution from transformed self-exemplars.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Single image super-resolution from transformed self-exemplars

Reference 23

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Observation 4f002911-9bb7-4f5d-a579-494aeb108905 · outbound

This paper cites Lightweight image super-resolution with information multi- distillation network.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Lightweight image super-resolution with information multi- distillation network

Reference 24

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Observation aa0583a4-b8d0-4920-bb1b-dfd70689b2b1 · outbound

This paper cites Accurate image super-resolution using very deep convolutional net- works.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Accurate image super-resolution using very deep convolutional net- works

Reference 25

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Observation 5f60161f-99de-479f-b47e-ca9c16ad5455 · outbound

This paper cites Segment any- thing.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Segment any- thing

Reference 26

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Observation 746e76e6-eff7-4c01-8ad1-15354475d08e · outbound

This paper cites Deep laplacian pyramid networks for fast and accurate super-resolution.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Deep laplacian pyramid networks for fast and accurate super-resolution

Reference 27

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This paper cites Lapar: Linearly-assembled pixel-adaptive re- gression network for single image super-resolution and be- yond.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Lapar: Linearly-assembled pixel-adaptive re- gression network for single image super-resolution and be- yond

Reference 28

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Observation 09566fb3-5104-4077-ad37-57142e9430b0 · outbound

This paper cites On Efficient Transformer-Based Image Pre-training for Low-Level Vision.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration On Efficient Transformer-Based Image Pre-training for Low-Level Vision

Reference 29

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Observation 806169e4-27ae-4739-b24f-6c0a2f897990 · outbound

This paper cites Efficient and explicit modelling of image hierarchies for image restora- tion.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Efficient and explicit modelling of image hierarchies for image restora- tion

Reference 30

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Observation bcede10f-e0ff-4e32-ba8c-2c8ebe95281d · outbound

This paper cites Lsdir: A large scale dataset for image restoration.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Lsdir: A large scale dataset for image restoration

Reference 31

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Observation 156a9d25-4e29-4103-9463-ab40e758a5c6 · outbound

This paper cites Swinir: Image restoration using swin transformer.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Swinir: Image restoration using swin transformer

Reference 32

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Observation e712d97b-cd1f-4ab1-83ad-2d1f39659023 · outbound

This paper cites Enhanced deep residual networks for single image super-resolution.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Enhanced deep residual networks for single image super-resolution

Reference 33

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Observation 7d26aca5-2a57-485e-bce3-c79e9d40e338 · outbound

This paper cites Microsoft coco: Common objects in context.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Microsoft coco: Common objects in context

Reference 34

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Observation d1ffa513-30bf-4be4-9b9a-639778c73523 · outbound

This paper cites VMamba: Visual State Space Model.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration VMamba: Visual State Space Model

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Swin transformer: Hierarchical vision transformer using shifted windows

Reference 36

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This paper cites Waterloo exploration database: New challenges for image quality as- sessment models.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Waterloo exploration database: New challenges for image quality as- sessment models

Reference 37

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Observation 740ceeb0-a937-4d8b-a867-5b227217ee1d · outbound

This paper cites A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

Reference 38

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Observation 28c5b817-29f8-4fab-8e1c-12cc4d64c036 · outbound

This paper cites Sketch-based manga retrieval using manga109 dataset.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Sketch-based manga retrieval using manga109 dataset

Reference 39

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Observation 8aed639b-84aa-4b05-8dd0-b46bbd6f82de · outbound

This paper cites Single image super-resolution via a holistic attention network.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Single image super-resolution via a holistic attention network

Reference 40

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Observation 0c1614da-c680-486e-894b-6f0811b74af1 · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration RWKV: Reinventing RNNs for the Transformer Era

Reference 41

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This paper cites Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence

Reference 42

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Toeplitz Neural Network for Sequence Modeling

Reference 43

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This paper cites Ntire 2017 challenge on single image super-resolution: Methods and results.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Ntire 2017 challenge on single image super-resolution: Methods and results

Reference 44

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Observation c0e19238-9322-4fbb-8e3b-9eec5074cbf2 · outbound

This paper cites Attention is all you need.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Attention is all you need

Reference 45

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Observation 6160ea7c-ccf2-41d4-a931-ad7a87bd0ed4 · outbound

This paper cites Selective structured state-spaces for long-form video understanding.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Selective structured state-spaces for long-form video understanding

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:08:14.627053Z

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Observation c8a66b21-0ca6-43eb-b260-13ee12491d6a · outbound

This paper cites Esrgan: En- hanced super-resolution generative adversarial networks.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Esrgan: En- hanced super-resolution generative adversarial networks

Reference 47

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Observation 90b523c9-cee5-4f3d-be5b-67964585f3ea · outbound

This paper cites Unsupervised real-world image super resolution via domain-distance aware training.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Unsupervised real-world image super resolution via domain-distance aware training

Reference 48

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Observation bb50100a-e8a0-4ffd-9aaf-b139abdc4a21 · outbound

This paper cites Incorporating convolution designs into visual transformers.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Incorporating convolution designs into visual transformers

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:08:14.586681Z

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Observation 1080947d-b52a-4ce1-b79b-abe82295e05b · outbound

This paper cites Restormer: Efficient transformer for high-resolution image restoration.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Restormer: Efficient transformer for high-resolution image restoration

Reference 50

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Observation 407d2c6b-a88d-4df9-b053-7add3691d399 · outbound

This paper cites On single image scale-up using sparse-representations.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration On single image scale-up using sparse-representations

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:08:14.556424Z

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Observation 376e200b-686d-491c-ab45-0a2b7242f5fa · outbound

This paper cites An attention free transformer, 2021.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration An attention free transformer, 2021

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:08:14.539734Z

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This paper cites Analogous to evolutionary algorithm: Design- ing a unified sequence model.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Analogous to evolutionary algorithm: Design- ing a unified sequence model

Reference 53

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Scsnet: An efficient paradigm for learning simultaneously image colorization and super- resolution

Reference 54

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Rethinking mobile block for efficient attention-based models

Reference 55

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Accurate image restoration with attention retractable transformer

Reference 56

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:08:14.432408Z

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Learning deep cnn denoiser prior for image restoration

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:08:14.414197Z

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Observation 80d2a74b-bbe7-4da1-b966-f5f43d53418c · outbound

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Ffdnet: Toward a fast and flexible solution for cnn-based image denoising

Reference 59

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Plug-and-play image restora- tion with deep denoiser prior

Reference 60

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Observation 6b7f48f1-dd45-4411-a557-9f50eb72bec6 · outbound

This paper cites Plug-and-play image restora- tion with deep denoiser prior.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Plug-and-play image restora- tion with deep denoiser prior

Reference 61

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Observation ec93c966-1d14-4850-88bb-f52abc635e6c · outbound

This paper cites Efficient long-range attention network for image super-resolution.

Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Efficient long-range attention network for image super-resolution

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:08:14.347688Z

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Image super-resolution using very deep residual channel attention networks

Reference 63

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Image super-resolution using very deep 11 residual channel attention networks

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:08:14.314544Z

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Observation 3931693f-9c5d-407c-90b4-9303233379d1 · outbound

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Residual dense network for image super-resolution

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:08:14.297228Z

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Observation 91f56121-41eb-44f6-90db-29df951a81c6 · outbound

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP

Reference 66

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Observation f15fa4a6-e22b-4e01-99aa-ca3a9a55e53a · outbound

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolution

Reference 67

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration These experiments show the generality of our model

Reference 69

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Observation c37bcc86-8a52-4dd1-8b17-24f6c3867053 · outbound

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Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration Unresolved cited work

Reference 1400

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Pith citing papers

Observation 35fb8aa7-2a08-49ca-ad69-5ce77e1ac94e · inbound

Pinco: Position-induced Consistent Adapter for Diffusion Transformer in Foreground-conditioned Inpainting cites this paper.

Pinco: Position-induced Consistent Adapter for Diffusion Transformer in Foreground-conditioned Inpainting Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration

Reference 15

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Observation 99b1daa6-e7a5-4e2d-a50d-b59854ec547f · inbound

A Survey of RWKV cites this paper.

A Survey of RWKV Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration

Reference 155

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Observation 029e2052-7c78-46a6-8952-0fe9fb573e9d · inbound

PestVL-Net: Enabling Multimodal Pest Learning via Fine-grained Vision-Language Interaction cites this paper.

PestVL-Net: Enabling Multimodal Pest Learning via Fine-grained Vision-Language Interaction Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration

Reference 7

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Observation ed08c703-cf92-4653-ae20-b518d653e7ed · inbound

CLUIE: Clustering-Aware Recurrent Propagation with Local Structural Compensation for Underwater Image Enhancement cites this paper.

CLUIE: Clustering-Aware Recurrent Propagation with Local Structural Compensation for Underwater Image Enhancement Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration

Reference 15

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