Paper Citation Record · LEDGER

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model

As of 20 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2502.03302.

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

pith.paper-citation-record.v1
2502.03302 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T05:15:53.261590Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4694d6f3-f3d8-4b3d-8445-6d9752933968 · outbound

This paper cites an unresolved cited work.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-09T05:15:53.683368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a1735c46-14f5-4108-a318-71709fdbe011 · outbound

This paper cites Plug-and-play priors for model based reconstruction,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Plug-and-play priors for model based reconstruction,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.667190Z

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Observation 3f17d70e-be55-439b-ad03-5adf2f53280b · outbound

This paper cites Plug- and-play unplugged: Optimization-free reconstruction using consensus equilibrium,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Plug- and-play unplugged: Optimization-free reconstruction using consensus equilibrium,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.649083Z

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Observation 3b0adbfe-a05e-4405-9a07-4b72a4263359 · outbound

This paper cites Plug-and-play methods for magnetic res- onance imaging: Using denoisers for image recovery,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Plug-and-play methods for magnetic res- onance imaging: Using denoisers for image recovery,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.631303Z

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Observation d27a6aae-cb7a-4939-8da8-b940722cd756 · outbound

This paper cites Memory-efficient model-based deep learning with convergence and robustness guarantees,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Memory-efficient model-based deep learning with convergence and robustness guarantees,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.613749Z

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Observation d84302d9-aad3-4347-b4f9-9bdd1573ae5e · outbound

This paper cites Plug-and- play methods provably converge with properly trained denoisers,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Plug-and- play methods provably converge with properly trained denoisers,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.596995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 65deee4e-b21e-4d64-a825-74cfb5509270 · outbound

This paper cites Multi-scale energy (muse) framework for inverse problems in imaging,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Multi-scale energy (muse) framework for inverse problems in imaging,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.581286Z

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Observation bd5d1713-bdbd-4db3-87ab-10597964882e · outbound

This paper cites Deep admm-net for compressive sensing mri,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Deep admm-net for compressive sensing mri,

Reference 8

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Source-reported events for the cited work

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Observation 28fb853e-8107-4f9f-8d7b-7475af97ac8c · outbound

This paper cites Learning a variational network for reconstruction of accelerated mri data,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Learning a variational network for reconstruction of accelerated mri data,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.553942Z

Source-reported events for the cited work

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Observation 2eb7b384-a927-4f9f-a75f-6a174359ef93 · outbound

This paper cites Modl: Model-based deep learning architecture for inverse problems,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Modl: Model-based deep learning architecture for inverse problems,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T05:15:53.190608Z

Source-reported events for the cited work

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Observation a33f7c03-900d-43ba-a31b-d126ce9efe4c · outbound

This paper cites Cinenet: deep learning-based 3d cardiac cine mri reconstruction with multi- coil complex-valued 4d spatio-temporal convolutions,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Cinenet: deep learning-based 3d cardiac cine mri reconstruction with multi- coil complex-valued 4d spatio-temporal convolutions,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.526399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8297f6e1-f520-4f6f-8e06-a14d67d513e2 · outbound

This paper cites It has potential: Gradient-driven denoisers for convergent solutions to inverse problems,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model It has potential: Gradient-driven denoisers for convergent solutions to inverse problems,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.510455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 237cd3ba-ef8d-472b-9d73-9b3e12b0b290 · outbound

This paper cites Gradient Step Denoiser for convergent Plug-and-Play.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Gradient Step Denoiser for convergent Plug-and-Play

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T05:15:53.205365Z

Source-reported events for the cited work

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Observation 8de62096-67a7-4608-b1db-b00b35c03775 · outbound

This paper cites Learned convex regularizers for inverse problems.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Learned convex regularizers for inverse problems

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T05:15:53.210625Z

Source-reported events for the cited work

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Observation 1f753b2f-a9f6-4981-8017-4a41d6527707 · outbound

This paper cites A neural-network-based convex regularizer for inverse problems,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model A neural-network-based convex regularizer for inverse problems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.492532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4f8848db-ffdf-4355-88a0-1e0a2f599a8b · outbound

This paper cites Learning weakly convex regu- larizers for convergent image-reconstruction algorithms,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Learning weakly convex regu- larizers for convergent image-reconstruction algorithms,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.474831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e6662ef6-3fc4-4efd-95c2-cbb10179af32 · outbound

This paper cites Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T05:15:53.224530Z

Source-reported events for the cited work

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Observation ff5477c0-24f2-4b33-9a4d-0bbc8722bcf1 · outbound

This paper cites A connection between score matching and denoising au- toencoders,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model A connection between score matching and denoising au- toencoders,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.458216Z

Source-reported events for the cited work

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Observation 0264bef3-119c-49bc-9a81-13583deccc62 · outbound

This paper cites an unresolved cited work.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T05:15:53.233688Z

Source-reported events for the cited work

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Observation 079afac8-40dd-4f72-9d44-4e2a45e6b0ce · outbound

This paper cites Spectral Normalization for Generative Adversarial Networks.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Spectral Normalization for Generative Adversarial Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T05:15:53.238123Z

Source-reported events for the cited work

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Observation 314c10b1-ace7-41b7-bae4-a6d10c732209 · outbound

This paper cites Clip: Cheap lipschitz training of neural networks,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Clip: Cheap lipschitz training of neural networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.431375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 48a7e164-e3d8-4c8a-87d8-48f56056280f · outbound

This paper cites Espirit—an eigenvalue approach to autocalibrating parallel mri: where sense meets grappa,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Espirit—an eigenvalue approach to autocalibrating parallel mri: where sense meets grappa,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.415235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 07928797-74be-4893-938f-00f5e2b9a9e2 · outbound

This paper cites fastMRI: An Open Dataset and Benchmarks for Accelerated MRI.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T05:15:53.251935Z

Source-reported events for the cited work

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Observation b285d8c2-4872-41c8-b3ce-560becfbe2af · outbound

This paper cites Sense: sensitivity encoding for fast mri,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Sense: sensitivity encoding for fast mri,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.399635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8d93fcef-ea56-4881-bdfd-49c2914d8b1f · outbound

This paper cites Deep equilibrium models,.

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model Deep equilibrium models,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:15:53.382997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.