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.
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Energy-based model with covariance regularization computes normalized posteriors for linear inverse problems without retraining, enabling adaptive sampling and blind estimation on image datasets.
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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.
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Learning Normalized Energy Models for Linear Inverse Problems
Energy-based model with covariance regularization computes normalized posteriors for linear inverse problems without retraining, enabling adaptive sampling and blind estimation on image datasets.