Abstract
Ill-posed imaging inverse problems remain challenging due to the ambiguity in mapping degraded observations to clean images. Diffusion-based generative priors have recently shown promise, but typically rely on computationally intensive iterative sampling or per-instance optimization. Amortized variational inference frameworks address this inefficiency by learning a direct mapping from measurements to posteriors, enabling fast posterior sampling without requiring the optimization of a new posterior for every new set of measurements. However, they still struggle to reconstruct fine details and complex textures. To address this, we extend the amortized framework by injecting spatially adaptive perturbations to measurements during training, guided by uncertainty estimates, to emphasize learning in the most uncertain regions. Experiments on deblurring and super-resolution demonstrate that our method achieves superior or competitive performance to previous diffusion-based approaches, delivering more realistic reconstructions without the computational cost of iterative refinement.
Abstract (translated)
不适定成像逆问题由于将退化观测映射到干净图像时的模糊性，仍然具有挑战性。基于扩散的生成先验最近显示出前景，但通常依赖于计算密集型的迭代采样或逐实例优化。预付变分推理框架通过学习从测量值直接映射到后验的方法来解决这种低效问题，从而能够无需为每组新测量值优化新的后验就快速进行后验采样。然而，它们仍然难以重建精细细节和复杂纹理。 为了应对这一挑战，我们扩展了预付框架，在训练期间注入由不确定性估计指导的空间自适应扰动，以强调在最不确定区域的学习过程。去模糊化和超分辨率的实验表明，我们的方法实现了优于或与先前基于扩散的方法相当的表现，提供了更为逼真的重建效果，并且避免了迭代细化带来的计算成本。
URL
https://arxiv.org/abs/2602.11704
