# Improving Decoupled Posterior Sampling for Inverse Problems using Data Consistency Constraint Source: [Improving Decoupled Posterior Sampling for Inverse Problems using Data Consistency Constraint]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:30:26.676Z Authors: Zhi Qi, Shihong Yuan, Yulin Yuan, Linling Kuang, Yoshiyuki Kabashima, Xiangming Meng Published: 2024-12-01T03:57:21.000Z ## Abstract Diffusion models have shown strong performances in solving inverse problems through posterior sampling while they suffer from errors during earlier steps. To mitigate this issue, several Decoupled Posterior Sampling methods have been recently proposed. However, the reverse process in these methods ignores measurement information, leading to errors that impede effective optimization in subsequent steps. To solve this problem, we propose Guided Decoupled Posterior Sampling (GDPS) by integrating a data consistency constraint in the reverse process. The constraint performs a smoother transition within the optimization process, facilitating a more effective convergence toward the target distribution. Furthermore, we extend our method to latent diffusion models and Tweedie's formula, demonstrating its scalability. We evaluate GDPS on the FFHQ and ImageNet datasets across various linear and nonlinear tasks under both standard and challenging conditions. Experimental results demonstrate that GDPS achieves state-of-the-art performance, improving accuracy over existing methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()