无需预训练，通过自扩散过程同时恢复图像和模糊核。
Self-Diffusion Driven Blind Imaging
- 从纯噪声开始，迭代反向自扩散重建图像与模糊核。
- 在光学畸变与运动模糊组合场景下性能超越现有方法。
- 零样本自监督，不依赖预训练或超参调优，适合实际成像场景。
光学成像系统因衍射极限、镜头制造公差、装配偏差等物理限制而固有缺陷。此外，不可避免的相机抖动和物体运动在采集过程中引入非理想退化。这些像差和运动引起的失真通常未知、难以测量，且在实践中建模或校准成本高昂。盲反问题通过联合估计潜在图像和未知退化核提供了有前景的解决方向。然而，现有方法常面临收敛不稳定、先验表达能力有限及对超参数敏感等问题。受自扩散最新进展启发，我们提出 DeblurSDI——一种零样本、自监督的盲成像框架，无需预训练。DeblurSDI 将盲图像恢复建模为从纯噪声开始的迭代反向自扩散过程，逐步精炼出清晰图像与模糊核。在组合光学畸变与运动模糊的大量实验中，DeblurSDI 均显著优于其他方法。
原文摘要 · Abstract (English)
Optical imaging systems are inherently imperfect due to diffraction limits, lens manufacturing tolerances, assembly misalignment, and other physical constraints. In addition, unavoidable camera shake and object motion further introduce non-ideal degradations during acquisition. These aberrations and motion-induced variations are typically unknown, difficult to measure, and costly to model or calibrate in practice. Blind inverse problems offer a promising direction by jointly estimating both the latent image and the unknown degradation kernel. However, existing approaches often suffer from convergence instability, limited prior expressiveness, and sensitivity to hyperparameters. Inspired by recent advances in self-diffusion, we propose DeblurSDI, a zero-shot, self-supervised blind imaging framework that requires no pre-training. DeblurSDI formulates blind image recovery as an iterative reverse self-diffusion process that begins from pure noise and progressively refines both the sharp image and the blur kernel. Extensive experiments on combined optical aberrations and motion blur demonstrate that DeblurSDI consistently outperforms other methods by a substantial margin.
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