Under a Gaussian prior assumption, zero-shot diffusion posterior samplers for inverse problems admit closed-form spectral representations that enable a new parameter-selection framework balancing perceptual quality and signal fidelity.
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Deep learning system synthesizes intermediate head CT slices to halve through-plane anisotropy while providing implicit denoising, outperforming baselines on structural metrics.
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Analyzing and Guiding Zero-Shot Posterior Sampling in Diffusion Models
Under a Gaussian prior assumption, zero-shot diffusion posterior samplers for inverse problems admit closed-form spectral representations that enable a new parameter-selection framework balancing perceptual quality and signal fidelity.
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Deep Slice Interpolation for Reducing Through-Plane Anisotropy and Noise in Head CT
Deep learning system synthesizes intermediate head CT slices to halve through-plane anisotropy while providing implicit denoising, outperforming baselines on structural metrics.