Paper Citation Record · LEDGER

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results

As of 6 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2509.06793.

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

pith.paper-citation-record.v1
2509.06793 v1

Coverage vector

measured 42 of 42 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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External citation measurements

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Outbound references

Observation 54767f37-7b25-402e-842c-c69d9a14f52d · outbound

This paper cites Simple baselines for image restoration.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Simple baselines for image restoration

Reference 1

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Observation afacc49a-3fc3-4f7b-9ad4-4ad3fc412cf3 · outbound

This paper cites Simple baselines for image restoration.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Simple baselines for image restoration

Reference 2

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Observation 3ecea4ab-6b2c-4c80-96f1-e87e7417698e · outbound

This paper cites Hierarchical integration diffusion model for realistic image deblurring.NeurIPS, 2024.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Hierarchical integration diffusion model for realistic image deblurring.NeurIPS, 2024

Reference 3

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This paper cites AIM 2025 high FPS non-uniform motion deblurring challenge report.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results AIM 2025 high FPS non-uniform motion deblurring challenge report

Reference 4

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This paper cites MIORe & V AR-MIORe: Benchmarks to push the boundaries of restoration.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results MIORe & V AR-MIORe: Benchmarks to push the boundaries of restoration

Reference 5

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Observation 86e979d3-3d08-4873-b0ee-2d8e0b561568 · outbound

This paper cites AIM 2025 rip current segmentation (RipSeg) challenge report.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results AIM 2025 rip current segmentation (RipSeg) challenge report

Reference 6

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Observation 73421850-3b75-49d4-9c0f-9f804049cd83 · outbound

This paper cites Efficient real-world deblurring using single images: AIM 2025 chal- lenge report.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Efficient real-world deblurring using single images: AIM 2025 chal- lenge report

Reference 7

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Observation ac92930a-bdd0-4c59-8765-0730001eefc7 · outbound

This paper cites 4K image super-resolution on mobile NPUs: Mobile AI & AIM 2025 challenge report.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results 4K image super-resolution on mobile NPUs: Mobile AI & AIM 2025 challenge report

Reference 8

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This paper cites Adapting stable diffusion for on-device inference: Mobile AI & AIM 2025 challenge report.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Adapting stable diffusion for on-device inference: Mobile AI & AIM 2025 challenge report

Reference 9

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Observation 8fd5953c-b756-460e-a945-7ef306e5e503 · outbound

This paper cites Efficient image denoising on smartphone GPUs: Mobile AI & AIM 2025 challenge report.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Efficient image denoising on smartphone GPUs: Mobile AI & AIM 2025 challenge report

Reference 10

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Observation adb25c94-edc8-40b0-81f8-bedc9c2deceb · outbound

This paper cites Efficient learned smartphone ISP on mobile GPUs: Mo- bile AI & AIM 2025 challenge report.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Efficient learned smartphone ISP on mobile GPUs: Mo- bile AI & AIM 2025 challenge report

Reference 11

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Observation 266aac56-9398-4287-aff5-632924f9ec73 · outbound

This paper cites AIM 2025 challenge on robust offline video super-resolution: Dataset, methods and results.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results AIM 2025 challenge on robust offline video super-resolution: Dataset, methods and results

Reference 12

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Observation ae33b832-f229-42a0-9d12-b64723e5da6e · outbound

This paper cites Efficient frequency domain-based trans- formers for high-quality image deblurring.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Efficient frequency domain-based trans- formers for high-quality image deblurring

Reference 13

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Observation 27d9857c-cfb0-4616-b09e-ada92bb878cf · outbound

This paper cites Efficient visual state space model for image deblurring.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Efficient visual state space model for image deblurring

Reference 14

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Observation 85858531-39a0-4723-a4e2-7c23cf0f8f91 · outbound

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AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Unresolved cited work

Reference 15

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Observation 90fe6350-8f07-42de-8d51-996ef1805984 · outbound

This paper cites Flux.1 kontext: Flow matching for in-context image generation and editing in latent space,.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Flux.1 kontext: Flow matching for in-context image generation and editing in latent space,

Reference 16

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This paper cites Real-world raw de- noising using diverse cameras: AIM 2025 challenge report.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Real-world raw de- noising using diverse cameras: AIM 2025 challenge report

Reference 17

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

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Swin transformer: Hierarchical vision transformer using shifted windows

Reference 18

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This paper cites AIM 2025 perceptual image super-resolution chal- lenge.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results AIM 2025 perceptual image super-resolution chal- lenge

Reference 19

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This paper cites SGDR: stochastic gradient descent with warm restarts.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results SGDR: stochastic gradient descent with warm restarts

Reference 20

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Observation f9c69678-79b0-48ac-b32c-325ed2f5294e · outbound

This paper cites Evenformer: Dynamic even transformer for real-world image restoration.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Evenformer: Dynamic even transformer for real-world image restoration

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:09:24.203725Z

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Observation 0d204204-4b7d-4966-bbd3-f73559191f98 · outbound

This paper cites Elucidating and Endowing the Diffusion Training Paradigm for General Image Restoration.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Elucidating and Endowing the Diffusion Training Paradigm for General Image Restoration

Reference 22

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Observation 38cb3745-c0e8-4d55-8528-3ab0a622d9a9 · outbound

This paper cites Boosting inverse tone mapping via diffusion regularization.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Boosting inverse tone mapping via diffusion regularization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:09:24.185461Z

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This paper cites Efficient high fps non-uniform mo- tion deblurring via progressive learning.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Efficient high fps non-uniform mo- tion deblurring via progressive learning

Reference 24

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This paper cites Continuous adverse weather removal via degradation-aware distillation.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Continuous adverse weather removal via degradation-aware distillation

Reference 25

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This paper cites Advancing ambient lighting nor- malization via diffusion shadow generation.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Advancing ambient lighting nor- malization via diffusion shadow generation

Reference 26

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AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Hirformer: Dynamic high resolution transformer for large-scale image shadow removal

Reference 27

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This paper cites Loformer: Local frequency transformer for im- age deblurring.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Loformer: Local frequency transformer for im- age deblurring

Reference 28

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This paper cites Deep multi-scale convolutional neural network for dynamic scene deblurring.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Deep multi-scale convolutional neural network for dynamic scene deblurring

Reference 29

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This paper cites Deep multi-scale convolutional neural network for dynamic scene deblurring.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Deep multi-scale convolutional neural network for dynamic scene deblurring

Reference 30

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This paper cites Real-world blur dataset for learning and benchmarking deblurring algorithms.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Real-world blur dataset for learning and benchmarking deblurring algorithms

Reference 31

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This paper cites AIM 2025 challenge on screen-content video quality assessment: Methods and results.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results AIM 2025 challenge on screen-content video quality assessment: Methods and results

Reference 32

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This paper cites Chronos 2.1-hd high-speed camera.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Chronos 2.1-hd high-speed camera

Reference 33

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This paper cites AIM 2025 challenge on inverse tone mapping report: Methods and results.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results AIM 2025 challenge on inverse tone mapping report: Methods and results

Reference 34

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This paper cites Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004

Reference 35

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This paper cites Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform.Patterns, 3(7):100543, 2022.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform.Patterns, 3(7):100543, 2022

Reference 36

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This paper cites AIM 2025 low-light raw video denoising challenge: Dataset, methods and results.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results AIM 2025 low-light raw video denoising challenge: Dataset, methods and results

Reference 37

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Observation 1e30642d-e3fc-4740-9851-3298dd20c1c9 · outbound

This paper cites Towards efficient and scale-robust ultra- high-definition image demoir´eing.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Towards efficient and scale-robust ultra- high-definition image demoir´eing

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:09:23.948084Z

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Observation cb5e1c64-bfbb-44e9-8958-2c538326e4f0 · outbound

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

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Restormer: Efficient transformer for high-resolution image restoration

Reference 39

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

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Restormer: Efficient transformer for high-resolution image restoration

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:09:23.920914Z

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Observation 4177f8aa-9382-4fea-b48a-ffcdd25fc7a6 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results The unreasonable effectiveness of deep features as a perceptual metric

Reference 41

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This paper cites EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer.

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T23:09:23.572748Z

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Pith citing papers

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