Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers | Paper Citation Record

Observation 459e6a1d-65ef-4cd7-af71-9e479f48e93d · outbound

This paper cites InverseProblems, 33(12):124007, 2017.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers InverseProblems, 33(12):124007, 2017

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Observation 34ee2678-9e72-4fc9-b6a5-f39f826d6ee0 · outbound

This paper cites Aliprantis and Kim C.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Aliprantis and Kim C

Reference 2

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Observation 43d30244-83f6-4be9-985c-7ec4e49e025c · outbound

This paper cites Solving inverse problems using data-driven models.ActaNumerica, 28:1–174, May 2019.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Solving inverse problems using data-driven models.ActaNumerica, 28:1–174, May 2019

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Observation abe30725-35bf-4138-a023-18eb2f71559b · outbound

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers A variational approach to removing multiplicative noise

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This paper cites DifferentialInclusions: Set-ValuedMapsandViabilityTheory.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers DifferentialInclusions: Set-ValuedMapsandViabilityTheory

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This paper cites ConvexAnalysisandMonotoneOperatorTheoryinHilbertSpaces.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers ConvexAnalysisandMonotoneOperatorTheoryinHilbertSpaces

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This paper cites Society for Industrial and Applied Mathematics, October 2017.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Society for Industrial and Applied Mathematics, October 2017

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Observation 0057990c-65ec-43b3-a367-0a2586c68e9f · outbound

This paper cites Modern regularization methods for inverse problems.Acta numerica, 27:1–111, 2018.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Modern regularization methods for inverse problems.Acta numerica, 27:1–111, 2018

Reference 8

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Observation 24d8578a-5b4a-4ff5-8c79-720836d238ef · outbound

This paper cites Deep unfolding of a proximal interior point method for image restoration.Inverse Problems, 36(3):034005, 2020.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Deep unfolding of a proximal interior point method for image restoration.Inverse Problems, 36(3):034005, 2020

Reference 9

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Observation d837f226-57c4-405d-9d16-f2273e6ee8bd · outbound

This paper cites Ontikhonovfunctionals penalizedby bregmandistances.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Ontikhonovfunctionals penalizedby bregmandistances

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Observation c7d5a720-67bf-43ed-850a-d289f2e20258 · outbound

This paper cites On the mean speed of convergence of empirical and oc- cupationmeasuresinWassersteindistance.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers On the mean speed of convergence of empirical and oc- cupationmeasuresinWassersteindistance

Reference 11

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Observation 2b051f22-febb-4003-a40e-ec3303f09526 · outbound

This paper cites Tweedie Moment Projected Diffusions For Inverse Problems.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Tweedie Moment Projected Diffusions For Inverse Problems

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Observation 4654faba-27db-45f7-9e92-651742527a14 · outbound

This paper cites Learning firmly nonexpansive operators.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Learning firmly nonexpansive operators

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Observation 51f3f626-46c2-4bbc-b82b-f383b6d9cd60 · outbound

This paper cites Total generalized variation.SIAM Journal on ImagingSciences, 3(3):492–526, January 2010.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Total generalized variation.SIAM Journal on ImagingSciences, 3(3):492–526, January 2010

Reference 14

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Observation 92717850-6e10-420d-b8f4-25c403df1477 · outbound

This paper cites Buskulic, M.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Buskulic, M

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Observation bdc109fb-4024-475c-b1c8-5ee55327950b · outbound

This paper cites Convergence and recovery guarantees of unsuper- visedneuralnetworksforinverseproblems.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Convergence and recovery guarantees of unsuper- visedneuralnetworksforinverseproblems

Reference 16

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Observation 3f52ab13-f6b0-4a17-8b1c-e47d6f6619c4 · outbound

This paper cites an unresolved cited work.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Unresolved cited work

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Observation 6b5722c2-0be9-439c-b954-a6f1081220fc · outbound

This paper cites Convergence guarantees of overparametrized wide deepinverseprior.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Convergence guarantees of overparametrized wide deepinverseprior

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Observation 2fd62fd0-078a-44a2-acb1-e9387a406254 · outbound

This paper cites Image recovery via total variation minimization and related problems.NumerischeMathematik, 76:167–188, 1997.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Image recovery via total variation minimization and related problems.NumerischeMathematik, 76:167–188, 1997

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Observation 9fbb65c6-c95b-4b71-9ab7-8b66618635ac · outbound

This paper cites Diffusion Posterior Sampling for General Noisy Inverse Problems.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Diffusion Posterior Sampling for General Noisy Inverse Problems

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Observation 6ffafa72-8735-4bb8-a717-c799d4149b6a · outbound

This paper cites Improving diffusion models for inverse problems using manifold constraints.Advances in Neural Information Processing Systems, 35:25683–25696, 2022.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Improving diffusion models for inverse problems using manifold constraints.Advances in Neural Information Processing Systems, 35:25683–25696, 2022

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Observation 7277216d-0d79-487d-97ef-31ed2e004242 · outbound

This paper cites Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data

Reference 22

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This paper cites Deep generative models and inverse problems.Mathematical Aspects of DeepLearning, 400, 2022.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Deep generative models and inverse problems.Mathematical Aspects of DeepLearning, 400, 2022

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Observation 2e0d5bb4-640d-4e70-b366-0810f5b689a8 · outbound

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Unresolved cited work

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This paper cites Regularising inverse problems with generative machine learning models.Journal of MathematicalImagingandVision, 66(1):37–56, 2024.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Regularising inverse problems with generative machine learning models.Journal of MathematicalImagingandVision, 66(1):37–56, 2024

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This paper cites Vincent Poor, and Shlomo Shamai Shitz.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Vincent Poor, and Shlomo Shamai Shitz

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Plug-and-playimagereconstructionisaconvergentregulariza- tion method.IEEETransactionsonImageProcessing, 33:1476–1486, 2024

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers ProximalLangevinsamplingwith inexact proximal mapping.SIAMJournal on ImagingSciences, 17(3):1729–1760, 2024

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Regularization of inverse problems.MathematicsanditsApplications, 375, 1996

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Esposito

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Falconer

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers On a formula for thel2 wasserstein metric between measures on euclidean and hilbert spaces.MathematischeNachrichten, 147:185–203, 1990

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Stein’sunbiasedriskestimate and hyv\" arinen’s score matching.arXivpreprint arXiv:2502.20123, 2025

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers The split bregman method for l1-regularized problems.SIAM Journal on ImagingSciences, 2(2):323–343, January 2009

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Learningfastapproximationsofsparsecoding

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Shouldpenalizedleastsquaresregressionbeinterpretedasmaximumaposteriori estimation? IEEE TransactionsonSignal Processing, 59(5):2405–2410, May 2011

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers priors" and

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Agem: Solvinglinearinverseproblemsviadeeppriors and sampling.Advancesin NeuralInformationProcessingSystems, 32, 2019

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Shamai, and S

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Estimationingaussiannoise: Proper- tiesoftheminimummean-squareerror

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Agenerativevariationalmodelforinverseproblemsinimaging

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Universal bayes consistency in metric spaces.Ann.Statist., 49(4):2129–2150, August 2021

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Hatsell and L

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers MachineLearningSolutions forInverseProblems: Part A, volume 26 ofHandbookof NumericalAnalysis

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Convergent regularizationininverseproblemsandlinearplug-and-playdenoisers

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Denoising diffusion probabilistic models.Advancesin neuralinformationprocessing systems, 33:6840–6851, 2020

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Convergentplug-and-play methods for image inverse problems with explicit and nonconvexdeep regularization

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Gradient Step Denoiser for convergent Plug-and-Play

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Proximaldenoiserforconvergentplug-and- play optimization with nonconvex regularization.arXivpreprint, 2022

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Estimationofnon-normalizedstatisticalmodelsbyscorematching

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Deep convolutional neuralnetworkforinverseproblemsinimaging

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers McCann, Emmanuel Froustey, and Michael Unser

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Stochastic mirror descent method for linear ill-posed problems in banach spaces.InverseProblems, 39(6):065010, May 2023

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Stochastic solutions for linear inverse problems using the prior implicit in a denoiser.Advancesin Neural Information Processing Systems, 34:13242–13254, 2021

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Plug-and-play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications.IEEESignal ProcessingMagazine, 40(1):85–97, 2023

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.487969Z

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Observation 543fc55b-cf60-41c2-912f-46c6cfff3096 · outbound

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Accurate image superresolution using very deep convolutional networks.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1646–1654, 2016

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Observation 85c835db-3619-4943-91df-565c6d44880d · outbound

This paper cites Total deep variation for linear inverse problems.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Total deep variation for linear inverse problems

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Observation 9197d307-92da-4014-a26a-c3a72b838cb3 · outbound

This paper cites Bayesian imaging using plug & play priors: when Langevin meets tweedie.SIAM Journal onImagingSciences, 15(2):701–737, 2022.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Bayesian imaging using plug & play priors: when Langevin meets tweedie.SIAM Journal onImagingSciences, 15(2):701–737, 2022

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.500568Z

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Observation ef7240ec-a9f0-4094-a313-a8542ec5d3a1 · outbound

This paper cites On maximum a posteriori estimation with plug & play priors and stochastic gradient de- scent.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers On maximum a posteriori estimation with plug & play priors and stochastic gradient de- scent

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Observation 7c3a8c4b-605d-4d2d-b9a2-30c4f2ce3726 · outbound

This paper cites Leterme, A.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Leterme, A

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.509556Z

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Observation 36eb0afd-5505-41c0-a03c-fa78b3860211 · outbound

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers NETT: solving inverse problems with deep neural networks.InverseProblems, 36(6):065005, June 2020

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.513845Z

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Observation 6791e4e2-dc04-4ddc-bb23-7197d8082c58 · outbound

This paper cites Recovery analysis for plug-and- play priors using the restricted eigenvalue condition.Advances in Neural Information Processing Systems, 34:5921–5933, 2021.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Recovery analysis for plug-and- play priors using the restricted eigenvalue condition.Advances in Neural Information Processing Systems, 34:5921–5933, 2021

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.517822Z

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Observation 5b903c8e-8b11-475a-837d-00375ce85501 · outbound

This paper cites Outersemicontinuityofpositivehullmappingswithapplication tosemi-infiniteandstochasticprogramming.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Outersemicontinuityofpositivehullmappingswithapplication tosemi-infiniteandstochasticprogramming

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.521783Z

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Observation 3b286675-9e5c-4e01-9325-c31a66937d2d · outbound

This paper cites Adversarialregularizersininverseprob- lems.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Adversarialregularizersininverseprob- lems

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.525736Z

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Observation 8a3c272c-5734-4a73-b5df-b5ad9e8f69c9 · outbound

This paper cites Elsevier, 1999.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Elsevier, 1999

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.529691Z

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Observation 8dd8c8c3-dad8-4c2a-8a0d-80800c602cf8 · outbound

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Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers An empirical bayes estimator of the mean of a normal population.Bull

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Observation db3b3491-db67-4b55-87b4-69de710ad70f · outbound

This paper cites Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing.IEEESignal ProcessingMagazine, 38(2):18–44, 2021.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing.IEEESignal ProcessingMagazine, 38(2):18–44, 2021

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.538139Z

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Observation 95f69bdb-e214-4a2b-a957-c3a171a4a77b · outbound

This paper cites Data-driven convex regularizersforinverseproblems.In ICASSP2024-2024IEEEInternationalConferenceonAcoustics, SpeechandSignalProcessing (ICASSP), pages 13386–13390.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Data-driven convex regularizersforinverseproblems.In ICASSP2024-2024IEEEInternationalConferenceonAcoustics, SpeechandSignalProcessing (ICASSP), pages 13386–13390

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.542222Z

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Observation c4756583-9601-479d-8762-2b024cbed239 · outbound

This paper cites End-to-end reconstructionmeetsdata-drivenregularizationforinverseproblems.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers End-to-end reconstructionmeetsdata-drivenregularizationforinverseproblems

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Observation b30404ab-0810-4b43-9594-287dd091dfc0 · outbound

This paper cites Learned reconstruction methods with convergence guarantees: a survey of concepts and applications.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Learned reconstruction methods with convergence guarantees: a survey of concepts and applications

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.550866Z

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Observation e44d3420-765a-4c0a-b969-3434e3998977 · outbound

This paper cites Chaudhury.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Chaudhury

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.555023Z

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Observation a2a81d5a-2143-4628-bc30-7934eb2cca1c · outbound

This paper cites Multiscale hierarchical decomposition of im- ageswithapplicationstodeblurring,denoising,andsegmentation.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Multiscale hierarchical decomposition of im- ageswithapplicationstodeblurring,denoising,andsegmentation

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.558997Z

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Observation cdfc9dc5-34d9-4fdd-8bf7-27f102031b08 · outbound

This paper cites An analytic theory of convo- lutional neural network inverse problems solvers.arXivpreprint, 2026.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers An analytic theory of convo- lutional neural network inverse problems solvers.arXivpreprint, 2026

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.562919Z

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Observation 01533b83-ed96-4bc8-b38e-5252e5592021 · outbound

This paper cites Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.567516Z

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Observation b7c36494-d924-48f4-921c-950911a52d48 · outbound

This paper cites Metzler, Richard G.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Metzler, Richard G

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.572219Z

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Observation 13cf2150-0eea-47af-b73d-fe955daa46e4 · outbound

This paper cites Aniterativeregularization methodfortotalvariation-basedimagerestoration.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Aniterativeregularization methodfortotalvariation-basedimagerestoration

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Observation b9867ada-50b6-420c-acb9-e497e878a37e · outbound

This paper cites Palomar and S.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Palomar and S

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Observation 153ad3ab-b3e7-4872-9b32-14f4f01a006f · outbound

This paper cites MAP estimation with denoisers: Convergence rates and guarantees.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers MAP estimation with denoisers: Convergence rates and guarantees

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.584566Z

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Observation 267b9d95-4fcf-4825-ba9a-8247eabef89d · outbound

This paper cites Optimalapproximationofpiecewisesmoothfunctionsusing deep relu neural networks.NeuralNetworks, 108:296–330, December 2018.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Optimalapproximationofpiecewisesmoothfunctionsusing deep relu neural networks.NeuralNetworks, 108:296–330, December 2018

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.588893Z

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Observation 44cf5058-bc51-425d-9793-8265d7271f5c · outbound

This paper cites Deep neural networks can stably solve high-dimensional, noisy, non-linear inverse problems.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Deep neural networks can stably solve high-dimensional, noisy, non-linear inverse problems

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Observation b5580a9a-5137-49f0-ad9a-e206cc2413ab · outbound

This paper cites TheVolumeofConvexBodiesandBanachSpaceGeometry.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers TheVolumeofConvexBodiesandBanachSpaceGeometry

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Observation 2eb237f2-ea48-4940-8def-d9b084ba67d0 · outbound

This paper cites Nonasymptotic Convergence Rates for Plug-and-Play Methods With MMSE Denoisers.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Nonasymptotic Convergence Rates for Plug-and-Play Methods With MMSE Denoisers

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.601559Z

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Observation 97c12a54-6183-4f3f-bfa2-de178e4a6ad2 · outbound

This paper cites Learninglocalregularization forvariationalimagerestoration.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Learninglocalregularization forvariationalimagerestoration

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.606357Z

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Observation e67cce76-8cfd-4853-8b37-c5e2a45e4a31 · outbound

This paper cites an unresolved cited work.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Unresolved cited work

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.610382Z

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Observation 4a238ddb-9c71-4780-9435-4692188ec331 · outbound

This paper cites Tyrrell Rockafellar and Roger J.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Tyrrell Rockafellar and Roger J

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.614300Z

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Observation ea1c7d05-aa73-4e27-b788-3bc10d1c86be · outbound

This paper cites MatrixAnalysis.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers MatrixAnalysis

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.618519Z

Source-reported events for the cited work

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Observation b005da11-3ff3-4996-8450-ff2680a06cd6 · outbound

This paper cites The little engine that could: Regularization by denoising (red).SIAMJournal onImagingSciences, 10(4):1804–1844, 2017.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers The little engine that could: Regularization by denoising (red).SIAMJournal onImagingSciences, 10(4):1804–1844, 2017

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.622564Z

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Observation 18feea3e-f805-480d-9adc-f3856e744815 · outbound

This paper cites Nonlinear total variation based noise removal algorithms.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Nonlinear total variation based noise removal algorithms

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Observation 65659d85-fbb4-45ca-9b2a-4d3ba7bfee09 · outbound

This paper cites Mc-Graw-Hill, Boston, 2nd edition, 1991.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Mc-Graw-Hill, Boston, 2nd edition, 1991

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.630634Z

Source-reported events for the cited work

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Observation 47192b92-0b5e-496d-a04b-bba66f61c67a · outbound

This paper cites Lipschitz regularity of deep neural networks: analysis and efficient estimation.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Lipschitz regularity of deep neural networks: analysis and efficient estimation

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.634500Z

Source-reported events for the cited work

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Observation 008e2aff-0a59-420b-bd97-d123796cbf41 · outbound

This paper cites Variationalmethodsin imaging, volume 167.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Variationalmethodsin imaging, volume 167

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.638436Z

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Observation cd8c1799-1bcb-4a2d-80b9-81cf1f470cd1 · outbound

This paper cites Regularization methods in banach spaces.RegularizationMethodsinBanachSpaces, 07 2012.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Regularization methods in banach spaces.RegularizationMethodsinBanachSpaces, 07 2012

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Observation e5eb7d14-e6ba-465d-9564-73f72272685c · outbound

This paper cites Deep null space learning for inverse problems: convergence analysis and rates.InverseProblems, 35(2):025008, 2019.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Deep null space learning for inverse problems: convergence analysis and rates.InverseProblems, 35(2):025008, 2019

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Observation 1d7ae43f-2f11-4a95-a577-8e9101263a21 · outbound

This paper cites Model-baseddeeplearning: Keyapproachesanddesignguidelines.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Model-baseddeeplearning: Keyapproachesanddesignguidelines

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Observation 06d147ef-e885-4d71-8481-3651bc65c94f · outbound

This paper cites Weakly con- vex regularisers for inverse problems: convergence of critical points and primal-dual optimisation.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Weakly con- vex regularisers for inverse problems: convergence of critical points and primal-dual optimisation

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Observation 523c48d5-b9d5-47b6-abb0-1acc20ef32cf · outbound

This paper cites Generativemodelingbyestimatinggradientsofthedatadistribution.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Generativemodelingbyestimatinggradientsofthedatadistribution

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Source-reported events for the cited work

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Observation cc776c89-4e3d-462b-b225-3f20860504e8 · outbound

This paper cites Solving Inverse Problems in Medical Imaging with Score-Based Generative Models.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

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Observation fc34396d-7a1b-4f5a-9cb0-af0498432d34 · outbound

This paper cites Higher order convergence rates for bregman iterated varia- tional regularization of inverse problems.NumerischeMathematik, 141(1):215–252, July 2018.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Higher order convergence rates for bregman iterated varia- tional regularization of inverse problems.NumerischeMathematik, 141(1):215–252, July 2018

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.668066Z

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Observation 88f94390-0e6a-4b96-86a1-dd36ac1809a7 · outbound

This paper cites Cambridge university press, 2010.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Cambridge university press, 2010

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Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:55.672179Z

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Observation 7d85a30a-7186-4db5-9411-87f4bd1eed12 · outbound

This paper cites Scalable plug-and-play admmwithconvergenceguarantees.

Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers Scalable plug-and-play admmwithconvergenceguarantees

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