Paper Citation Record · LEDGER

Unrolling Nonconvex Graph Total Variation for Image Denoising

As of 12 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2506.02381.

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pith.paper-citation-record.v1
2506.02381 v1

Coverage vector

measured 35 of 35 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: pith, observed 2026-08-07T11:33:42.481967Z

Reference resolution

35 of 35 outbound references displayed

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External citation measurements

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Outbound references

Observation b7efbc80-a0cb-4ddd-926a-c38b55b8efd4 · outbound

This paper cites black boxes.

Unrolling Nonconvex Graph Total Variation for Image Denoising black boxes

Reference 1

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Observation 64e0c025-f87e-4d53-9919-a78c079b1bf2 · outbound

This paper cites Unrolling Nonconvex Graph Total Variation for Image Denoising.

Unrolling Nonconvex Graph Total Variation for Image Denoising Unrolling Nonconvex Graph Total Variation for Image Denoising

Reference 2

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This paper cites tightest.

Unrolling Nonconvex Graph Total Variation for Image Denoising tightest

Reference 3

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Unrolling Nonconvex Graph Total Variation for Image Denoising Unresolved cited work

Reference 4

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This paper cites GSP Definitions A positive undirected graphG= (V,E,W)is defined by a node setV={1,.

Unrolling Nonconvex Graph Total Variation for Image Denoising GSP Definitions A positive undirected graphG= (V,E,W)is defined by a node setV={1,

Reference 5

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Observation 5e259649-31d4-4b2a-8cdb-00099a8d6168 · outbound

This paper cites Defining Graph Huber Function 3.1.1.

Unrolling Nonconvex Graph Total Variation for Image Denoising Defining Graph Huber Function 3.1.1

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:49.234510Z

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This paper cites In addition, we learn asimilarity graphGfrom data, so that incidence matrixCspecifying a positive graphGin (13) is properly defined.

Unrolling Nonconvex Graph Total Variation for Image Denoising In addition, we learn asimilarity graphGfrom data, so that incidence matrixCspecifying a positive graphGin (13) is properly defined

Reference 7

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This paper cites Experimental Setup We set the number of NC-GTV layers toT= 2.CNN f consists of4convolution layers.

Unrolling Nonconvex Graph Total Variation for Image Denoising Experimental Setup We set the number of NC-GTV layers toT= 2.CNN f consists of4convolution layers

Reference 8

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This paper cites We show that our graph Huber function can be understood as a Moreau envelope.

Unrolling Nonconvex Graph Total Variation for Image Denoising We show that our graph Huber function can be understood as a Moreau envelope

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:48.541916Z

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Observation 1ff360d7-1cfb-4f76-9796-f177c776f2c0 · outbound

This paper cites A tour of modern image filtering: New insights and methods, both practical and theoretical,.

Unrolling Nonconvex Graph Total Variation for Image Denoising A tour of modern image filtering: New insights and methods, both practical and theoretical,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:48.303861Z

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Observation 6cdc2259-9cee-4d9c-86ba-9a8fc10d8b25 · outbound

This paper cites Graph-based blind image deblurring from a single photograph,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Graph-based blind image deblurring from a single photograph,

Reference 11

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This paper cites Plug-and-play ADMM for image restoration: Fixed-point convergence and applications,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Plug-and-play ADMM for image restoration: Fixed-point convergence and applications,

Reference 12

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Observation a4d41f87-2c98-4edc-ad3b-6b798e0c51d2 · outbound

This paper cites Tutorial on Diffusion Models for Imaging and Vision.

Unrolling Nonconvex Graph Total Variation for Image Denoising Tutorial on Diffusion Models for Imaging and Vision

Reference 13

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Observation 2b93c383-d5a9-4bf2-a1ba-4dfd7f79e322 · outbound

This paper cites Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,

Reference 14

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Observation 81717e30-3b0c-4e66-940e-d772a6db8a8e · outbound

This paper cites Algorithm unrolling: Inter- pretable, efficient deep learning for signal and image process- ing,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Algorithm unrolling: Inter- pretable, efficient deep learning for signal and image process- ing,

Reference 15

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Observation ae2d745d-94c0-43e8-a069-cc829e64d776 · outbound

This paper cites Graph signal processing: Overview, chal- lenges, and applications,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Graph signal processing: Overview, chal- lenges, and applications,

Reference 16

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Observation 042b2e77-1bd7-4d19-9963-19e06ceb9858 · outbound

This paper cites Graph spectral image processing,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Graph spectral image processing,

Reference 17

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Observation bd215079-0215-4d52-ac52-3fb5dcd1af08 · outbound

This paper cites Edge-preserving and scale-dependent properties of total variation regularization,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Edge-preserving and scale-dependent properties of total variation regularization,

Reference 18

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Observation 5efc51cb-0d2a-4ace-acd5-ea46a8b4a056 · outbound

This paper cites Non-convex total variation regularization for convex denoising of signals,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Non-convex total variation regularization for convex denoising of signals,

Reference 19

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Observation 09a5dd91-25a1-4ac9-bb7c-bfc247595032 · outbound

This paper cites Convex 1-D total vari- ation denoising with non-convex regularization,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Convex 1-D total vari- ation denoising with non-convex regularization,

Reference 20

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Observation 0a07a043-4d2d-48dc-912b-5411da07212c · outbound

This paper cites Sparsity-inducing nonconvex nonseparable regularization for convex image processing,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Sparsity-inducing nonconvex nonseparable regularization for convex image processing,

Reference 21

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Observation a814d9c6-1ab9-4e7d-85dc-344ed9074c4f · outbound

This paper cites Robust estimation of a location parameter,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Robust estimation of a location parameter,

Reference 22

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Unrolling Nonconvex Graph Total Variation for Image Denoising Unresolved cited work

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Reference 24

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Observation 82e8fd34-d8d5-4b93-91df-69571a6c4c47 · outbound

This paper cites Proximal algorithms,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Proximal algorithms,

Reference 25

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This paper cites Image denoising by sparse 3-D transform-domain collaborative filter- ing,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Image denoising by sparse 3-D transform-domain collaborative filter- ing,

Reference 26

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Observation 0b720747-8c34-43de-9a90-78bc1c145cba · outbound

This paper cites Graph Laplacian regularization for inverse imaging: Analysis in the continuous domain,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Graph Laplacian regularization for inverse imaging: Analysis in the continuous domain,

Reference 27

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Observation b9553c6f-7571-4d9b-8b0a-9a1fc54ee13d · outbound

This paper cites Random walk graph Laplacian based smoothness prior for soft decoding of JPEG images,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Random walk graph Laplacian based smoothness prior for soft decoding of JPEG images,

Reference 28

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Observation 58dccd29-15c2-4b77-b93c-45d69d5453a8 · outbound

This paper cites Nonlocal discrete regularization on weighted graphs: A framework for image and manifold processing,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Nonlocal discrete regularization on weighted graphs: A framework for image and manifold processing,

Reference 29

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This paper cites Dual constrained TV-based regularization on graphs,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Dual constrained TV-based regularization on graphs,

Reference 30

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Unrolling Nonconvex Graph Total Variation for Image Denoising Unresolved cited work

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This paper cites Boyd and L.

Unrolling Nonconvex Graph Total Variation for Image Denoising Boyd and L

Reference 32

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Observation 7c934ed1-bb1a-4e37-9fac-a16bd316068f · outbound

This paper cites An introduction to the conjugate gradient method without the agonizing pain,.

Unrolling Nonconvex Graph Total Variation for Image Denoising An introduction to the conjugate gradient method without the agonizing pain,

Reference 33

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Observation 65d75c2a-7d8a-46b0-afef-4860b699d07f · outbound

This paper cites Unrolling of deep graph total variation for image denoising,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Unrolling of deep graph total variation for image denoising,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:43.089937Z

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Observation c420a9bf-348d-4ddf-b3df-8065e5fa8dba · outbound

This paper cites Deep graph Laplacian regularization for robust denoising of real im- ages,.

Unrolling Nonconvex Graph Total Variation for Image Denoising Deep graph Laplacian regularization for robust denoising of real im- ages,

Reference 35

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Pith citing papers

Observation 64e0c025-f87e-4d53-9919-a78c079b1bf2 · inbound