Abstract
This report studies diffusion posterior sampling (DPS) for single-image super-resolution (SISR) under a known degradation model. We implement a likelihood-guided sampling procedure that combines an unconditional diffusion prior with gradient-based conditioning to enforce measurement consistency for $4\times$ super-resolution with additive Gaussian noise. We evaluate posterior sampling (PS) conditioning across guidance scales and noise levels, using PSNR and SSIM as fidelity metrics and a combined selection score $(\mathrm{PSNR}/40)+\mathrm{SSIM}$. Our ablation shows that moderate guidance improves reconstruction quality, with the best configuration achieved at PS scale $0.95$ and noise standard deviation $\sigma=0.01$ (score $1.45231$). Qualitative results confirm that the selected PS setting restores sharper edges and more coherent facial details compared to the downsampled inputs, while alternative conditioning strategies (e.g., MCG and PS-annealed) exhibit different texture fidelity trade-offs. These findings highlight the importance of balancing diffusion priors and measurement-gradient strength to obtain stable, high-quality reconstructions without retraining the diffusion model for each operator.
Abstract (translated)
这份报告研究了在已知退化模型下的单幅图像超分辨率（SISR）中的扩散后验采样（DPS）。我们实现了一种似然引导的采样过程，该过程结合了一个无条件扩散先验与基于梯度的条件处理来强制执行$4\times$超分辨率中加性高斯噪声的一致性测量。我们在指导比例和噪声水平上评估后验采样的（PS）条件，并使用峰值信噪比（PSNR）和结构相似性指数（SSIM）作为保真度指标，以及一个组合选择分数$(\mathrm{PSNR}/40)+\mathrm{SSIM}$来衡量性能。我们的消融研究表明，适度的指导可以提高重建质量，在PS比例为$0.95$且噪声标准差$\sigma=0.01$时达到最佳配置（得分为$1.45231$）。定性结果表明，所选的PS设置恢复了更清晰的边缘和更为一致的人脸细节，这与降采样的输入相比得到了改进。相比之下，其他条件处理策略（例如MCG和PS退火）表现出不同的纹理保真度权衡。这些发现强调了在不针对每个操作器重新训练扩散模型的情况下，为了获得稳定且高质量的重建结果，平衡扩散先验和测量梯度强度的重要性。
URL
https://arxiv.org/abs/2512.21797
