将重加权最小二乘法集成到插值扩散先验中用于噪声图像恢复

现有的插值式图像恢复方法通常采用即插即用的高斯去噪器作为变量分割框架中基于经典优化方法的proximal算子。最近，由生成式先验诱导的去噪器成功地集成到用于高斯噪声下的图像恢复中的正则化优化方法中。然而，其针对非高斯噪声——如冲击噪声——的应用仍然鲜为人知。本文提出了一种基于生成式扩散先验的插值式图像恢复框架，用于robust去除各种类型的噪声，包括冲击噪声。在最大后验(MAP)估计框架内，数据保真项适应特定的噪声模型。 departing from the conventional least-squares loss used for Gaussian noise, we introduce a generalized Gaussian scale mixture-based loss, which approximates a wide range of noise distributions and leads to an $\ell_q$-norm ($0<q\leq2$) fidelity term. This optimization problem is addressed using an iteratively reweighted least squares (IRLS) approach, wherein the proximal step involving the generative prior is efficiently performed via a diffusion-based denoiser. Experimental results on benchmark datasets demonstrate that the proposed method effectively removes non-Gaussian impulse noise and achieves superior restoration performance.