Expose, Then Commit: Diffusion Posterior Sampling

Diffusion posterior sampling solves inverse problems by combining a pretrained diffusion prior with measurement-consistency guidance. However, at high noise levels, clean-estimate errors can compromise full-band guidance, while stochastic refinement can modify details weakly constrained by the measurements. We argue that posterior sampling should coordinate measurement-frequency exposure during refinement with correction commitment afterward. Based on this principle, we propose a posterior continuation framework that constructs a family of intermediate posteriors whose noise-conditioned likelihoods emphasize low-frequency residuals and gradually return to full-band consistency. We instantiate this framework with a stabilized sampler combining a diffusion predictor, frequency-weighted Langevin refinement, and a Haar-domain commitment rule that fully adopts coarse corrections while progressively admitting detail corrections relative to the predictor. Across super-resolution, inpainting, and deblurring, our method achieves competitive-to-state-of-the-art restoration performance, including PSNR gains exceeding 5 dB on motion deblurring against the strongest evaluated baselines.