ML-SPnP accelerates stochastic PnP for SVCT by using MRA approximation spaces where prior-coherence corrections vanish in expectation, yielding comparable quality at reduced runtime.
The Little Engine That Could: Regularization by Denoising (RED)
4 Pith papers cite this work, alongside 901 external citations. Polarity classification is still indexing.
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Pith papers citing it
901
external citations · OpenAlex
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UNVERDICTED 4representative citing papers
RED is adapted to graph signals with deep unrolling for parameter estimation, yielding lower MSE than prior graph denoising methods on synthetic and real data.
MscaleFNO learns mappings from oscillatory media to wavefields for Helmholtz inverse problems and pairs it with diffusion regularization for partial-aperture 2D reconstructions.
Piecewise guidance in diffusion posterior sampling cuts inference time 23-25% on inpainting and super-resolution with negligible PSNR/SSIM loss while handling measurement noise.
citing papers explorer
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Multilevel Stochastic Plug-and-Play for Sparse-View CT Reconstruction
ML-SPnP accelerates stochastic PnP for SVCT by using MRA approximation spaces where prior-coherence corrections vanish in expectation, yielding comparable quality at reduced runtime.
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Graph Signal Denoising Using Regularization by Denoising and Its Parameter Estimation
RED is adapted to graph signals with deep unrolling for parameter estimation, yielding lower MSE than prior graph denoising methods on synthetic and real data.
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Multiscale Fourier Neural Operator for Inverse Wave Scattering in Highly Oscillatory Media
MscaleFNO learns mappings from oscillatory media to wavefields for Helmholtz inverse problems and pairs it with diffusion regularization for partial-aperture 2D reconstructions.
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Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance
Piecewise guidance in diffusion posterior sampling cuts inference time 23-25% on inpainting and super-resolution with negligible PSNR/SSIM loss while handling measurement noise.