对于从事图像恢复、超分辨率等逆问题的研究者，MAP-RPS提供了一种无需重新训练即可在推理时灵活调节失真与感知质量的方法，值得尝试。
该论文提出了一种名为MAP-RPS的阶段式框架，用于在扩散模型的零样本逆问题求解中实现失真-感知（D-P）权衡的灵活遍历。该方法先通过MAP估计阶段近似MMSE解，提供低失真初始化，再通过重噪后验采样阶段逐步提升感知质量。理论分析验证了设计的有效性，并扩展至潜在空间（LMAP-RPS），利用大规模预训练潜在扩散骨干。实验表明，该方法在多种任务上实现了更有效的D-P遍历，并作为高效求解器表现出色。
Stage-wise Distortion-Perception Traversal in Zero-shot Inverse Problems with Diffusion Models
The distortion-perception (D-P) tradeoff is a fundamental phenomenon of Bayesian inverse problems, which characterizes the inherent tension between distortion performance and perceptual quality. Enabling flexible traversal of the D-P tradeoff at inference time is crucial for practical applications. Despite the recent success of diffusion models in zero-shot inverse problem solving, efficient and principled strategies for D-P traversal in diffusion-based inverse algorithms remain inadequately characterized. In this paper, we propose a stage-wise framework for realizing D-P traversal using a single diffusion model in zero-shot inverse problems. Our proposed method, termed MAP-RPS, starts with an MAP estimation stage that approximates the MMSE solution and provides a low-distortion initialization, followed by a re-noised posterior sampling stage that progressively improves perceptual quality. We provide theoretical analyses for both stages, establishing the validity and effectiveness of the proposed design. Furthermore, we extend MAP-RPS to the latent space, yielding LMAP-RPS, which enjoys broader applicability by leveraging large-scale pre-trained latent diffusion backbones. Extensive experiments demonstrate that MAP-RPS and LMAP-RPS enable more effective D-P traversal on various tasks, while also exhibiting strong performance as efficient solvers for real-world inverse problems.