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Paper Citation Record · LEDGER

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

As of 10 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

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Pith citing papers itemized under the disclosed page cap.

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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

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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

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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

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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

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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

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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

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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

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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

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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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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

Reference 19

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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

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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

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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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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

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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

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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

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