Diffusion posterior samplers produce biased outputs that can be expressed as an Ornstein-Uhlenbeck path expectation via a surrogate Gaussian path and Feynman-Kac representation, with STSL flattening the spatially varying bias term.
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ProxiMAP enhances PnP restoration by using a noise schedule that keeps the denoiser in-distribution for reliable MAP approximation, yielding sharper images than standard MMSE or direct MAP targeting.
citing papers explorer
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Diffusion-Based Posterior Sampling: A Feynman-Kac Analysis of Bias and Stability
Diffusion posterior samplers produce biased outputs that can be expressed as an Ornstein-Uhlenbeck path expectation via a surrogate Gaussian path and Feynman-Kac representation, with STSL flattening the spatially varying bias term.
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Beyond MMSE: Enhancing PnP Restoration with ProxiMAP
ProxiMAP enhances PnP restoration by using a noise schedule that keeps the denoiser in-distribution for reliable MAP approximation, yielding sharper images than standard MMSE or direct MAP targeting.