超越MMSE：通过ProxiMAP增强PnP修复
Beyond MMSE: Enhancing PnP Restoration with ProxiMAP
AI总结：
本文提出ProxiMAP，通过调整噪声调度使去噪器保持分布内，实现更稳定的图像重建，适用于去模糊、补全、超分辨率和相位恢复等任务。
摘要：
Plug-and-Play (PnP)方法通过将不可行的最大后验（MAP）去噪器替换为MMSE去噪器成为解决成像逆问题的标准工具。尽管这种不匹配常被视为不可避免，近期研究试图通过针对扩散模型分数来缩小这一差距。本文指出在实践中，学习到的分数与真实分数不匹配，导致MAP目标迭代收敛到卡通化图像而非真实图像，而提前停止迭代能获得更好结果。本文将这一观察转化为设计原则，引入ProxiMAP，一种迭代的MAP近似方法，其噪声调度保持迭代残差噪声与去噪器训练噪声匹配。这使去噪器保持分布内，其分数可靠，并产生隐式提前停止，避免上述失败模式。ProxiMAP是标准PnP算法中MMSE去噪器的模块化替换，能一致提升重建质量。基于相同原理，本文提出一种混合变体，仅在PnP晚期迭代中应用ProxiMAP，其中去噪器最可靠，匹配或超过全替换变体，且成本仅为分数之一。
英文摘要：
Plug-and-Play (PnP) methods have become standard tools for solving imaging inverse problems by replacing the intractable maximum a posteriori (MAP) denoiser with the MMSE one. While this mismatch has been widely treated as unavoidable, recent works have sought to close this gap by targeting the MAP with diffusion-model scores. We show this is problematic in practice: learned scores do not match the true ones, so MAP-targeting iterations converge to cartoon-like images rather than realistic ones, and better results are obtained by stopping short of convergence. We turn this observation into a design principle and introduce ProxiMAP, an iterative MAP approximation whose noise schedule keeps the iterate's residual noise matched to the denoiser's training noise. This keeps the denoiser in-distribution where its score is reliable, and yields implicit early stopping that avoids the failure mode above. ProxiMAP is a modular drop-in replacement for MMSE denoisers in standard PnP algorithms and consistently sharpens reconstructions across deblurring, inpainting, super-resolution, and phase retrieval. Building on the same principle, we propose a hybrid variant that applies ProxiMAP only in the late iterations of PnP, where the denoiser is most reliable -- matching or exceeding the full-replacement variant at a fraction of the cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
