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Paper Citation Record · LEDGER

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime

As of 13 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2411.18189.

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pith.paper-citation-record.v1
2411.18189 v1

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measured 33 of 33 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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33 of 33 outbound references displayed

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External citation measurements

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Outbound references

Observation 87e65fa0-9634-47be-a916-4c42bbec7e67 · outbound

This paper cites Diffusercam: lensless single-exposure 3d imag- ing,.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Diffusercam: lensless single-exposure 3d imag- ing,

Reference 1

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This paper cites Generative adversarial nets,.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Generative adversarial nets,

Reference 2

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This paper cites Compressed sensing using generative models,.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Compressed sensing using generative models,

Reference 3

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This paper cites Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk

Reference 4

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This paper cites Huang, P.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Huang, P

Reference 5

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This paper cites Image-adaptive gan based reconstruction,.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Image-adaptive gan based reconstruction,

Reference 6

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This paper cites Deep compressed sensing,.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Deep compressed sensing,

Reference 7

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Observation 4f570297-a281-4a7f-bea6-9a1c8094f9a5 · outbound

This paper cites Phase retrieval under a generative prior,.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Phase retrieval under a generative prior,

Reference 8

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This paper cites Robust Compressive Phase Retrieval via Deep Generative Priors.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Robust Compressive Phase Retrieval via Deep Generative Priors

Reference 9

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This paper cites Blind image decon- volution using deep generative priors,.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Blind image decon- volution using deep generative priors,

Reference 10

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This paper cites Global Guarantees for Blind Demodulation with Generative Priors.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Global Guarantees for Blind Demodulation with Generative Priors

Reference 11

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This paper cites Flatcam: Thin, lensless cameras using coded aperture and computation,.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Flatcam: Thin, lensless cameras using coded aperture and computation,

Reference 12

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Deep image prior,

Reference 13

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Deep decoder: Concise image repre- sentations from untrained non-convolutional networks,

Reference 14

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This paper cites Untrained networks for compressive lensless photography,.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Untrained networks for compressive lensless photography,

Reference 15

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This paper cites Physics-informed deep deblurring: Over-parameterized vs. under-parameterized,.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Physics-informed deep deblurring: Over-parameterized vs. under-parameterized,

Reference 16

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This paper cites Low Shot Learning with Untrained Neural Networks for Imaging Inverse Problems.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Low Shot Learning with Untrained Neural Networks for Imaging Inverse Problems

Reference 17

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Reconstruct- ing synthetic lensless images in the low-data regime.,

Reference 18

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Nonlinear total vari- ation based noise removal algorithms,

Reference 19

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime A fast iterative shrinkage- thresholding algorithm for linear inverse problems,

Reference 20

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Dis- tributed optimization and statistical learning via the alternating direction method of multipliers,

Reference 21

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Regularizing linear inverse problems with convolutional neural networks

Reference 22

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Algorithmic guarantees for inverse imaging with untrained network priors,

Reference 23

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Observation e2a4d708-b736-4965-85ec-7f071fe28794 · outbound

This paper cites Compressed Sensing with Deep Image Prior and Learned Regularization.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Compressed Sensing with Deep Image Prior and Learned Regularization

Reference 24

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This paper cites Physics-driven machine learning for computational imaging 12 [from the guest editor],.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Physics-driven machine learning for computational imaging 12 [from the guest editor],

Reference 25

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Physics-informed deep learning for dual-energy com- puted tomography image processing,

Reference 26

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime On the interplay between physical and content priors in deep learning for computational imaging,

Reference 27

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Physics-informed machine learning,

Reference 28

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This paper cites Robust reconstruction with deep learning to handle model mismatch in lensless imaging,.

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Robust reconstruction with deep learning to handle model mismatch in lensless imaging,

Reference 29

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Implicit neural representations with periodic activation functions,

Reference 30

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Nerp: implicit neural repre- sentation learning with prior embedding for sparsely sampled image reconstruction,

Reference 31

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Learned reconstructions for practical mask-based lensless imaging,

Reference 32

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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime Microorganism preprocessed images dataset,

Reference 33

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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.

source=pdf_text observed=2026-08-12T11:29:02.925981Z digest=sha256:ec304ef8717bf7a7aea23832f1f8fbd8779a809ec739728b54bd581561fea4e8

Pith citing papers

No inbound Pith citation observations are available.