Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained generative models as modular priors. However, we identify a critical flaw in prevailing PnP solvers (e.g., based on HQS or Proximal Gradient): they function as memoryless operators, updating estimates solely based on instantaneous gradients. This lack of historical tracking inevitably leads to non-vanishing steady-state bias, where the reconstruction fails to strictly satisfy physical measurements under heavy corruption. To resolve this, we propose Dual-Coupled PnP Diffusion, which restores the classical dual variable to provide integral feedback, theoretically guaranteeing asymptotic convergence to the exact data manifold. However, this rigorous geometric coupling introduces a secondary challenge: the accumulated dual residuals exhibit spectrally colored, structured artifacts that violate the Additive White Gaussian Noise (AWGN) assumption of diffusion priors, causing severe hallucinations. To bridge this gap, we introduce Spectral Homogenization (SH), a frequency-domain adaptation mechanism that modulates these structured residuals into statistically compliant pseudo-AWGN inputs. This effectively aligns the solver's rigorous optimization trajectory with the denoiser's valid statistical manifold. Extensive experiments on CT and MRI reconstruction demonstrate that our approach resolves the bias-hallucination trade-off, achieving state-of-the-art fidelity with significantly accelerated convergence.

翻译：即插即用扩散先验（PnPDP）框架通过将预训练生成模型作为模块化先验，已成为解决成像逆问题的强大范式。然而，我们发现主流PnP求解器（例如基于HQS或近端梯度的方法）存在一个关键缺陷：它们作为无记忆算子运行，仅基于瞬时梯度更新估计值。这种历史追踪的缺失不可避免地导致非零稳态偏差，使得重建结果在严重噪声污染下无法严格满足物理测量约束。为解决此问题，我们提出双耦合PnP扩散方法，该方法通过恢复经典对偶变量提供积分反馈，从理论上保证渐近收敛到精确数据流形。然而，这种严格的几何耦合引入了新的挑战：累积的对偶残差呈现出频谱着色、结构化的伪影，这违反了扩散先验的加性高斯白噪声（AWGN）假设，导致严重的幻象生成。为弥合这一差距，我们提出频谱均匀化（SH）机制——一种频域自适应方法，可将这些结构化残差调制为统计合规的伪AWGN输入。这有效对齐了求解器的严格优化轨迹与去噪器的有效统计流形。在CT和MRI重建上的大量实验表明，我们的方法解决了偏差-幻象权衡问题，以显著加速的收敛速度实现了最先进的保真度。