MMSE denoisers correspond to 1-weakly convex regularizers via upper Moreau envelopes of negative log-marginals, enabling the first sublinear convergence rates for PnP proximal gradient descent.
Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,
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The work introduces a residual noise learning framework for cross-dose PET denoising that avoids averaged mappings by estimating noise directly from low-dose inputs and shows gains over one-size-for-all and dose-specific baselines on multi-center data.
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Nonasymptotic Convergence Rates for Plug-and-Play Methods With MMSE Denoisers
MMSE denoisers correspond to 1-weakly convex regularizers via upper Moreau envelopes of negative log-marginals, enabling the first sublinear convergence rates for PnP proximal gradient descent.
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Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning
The work introduces a residual noise learning framework for cross-dose PET denoising that avoids averaged mappings by estimating noise directly from low-dose inputs and shows gains over one-size-for-all and dose-specific baselines on multi-center data.