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.

翻译：不适定的成像逆问题由于从退化观测到清晰图像的映射存在模糊性而仍然具有挑战性。基于扩散的生成先验方法近期显示出潜力，但通常依赖于计算密集的迭代采样或针对每个实例的优化。摊销变分推断框架通过学习从测量值到后验分布的直接映射来解决这种低效问题，从而无需为每个新的测量集优化新的后验分布即可实现快速后验采样。然而，这些方法在重建精细细节和复杂纹理方面仍存在困难。为解决此问题，我们扩展了摊销框架，在训练期间根据不确定性估计的指导，对测量值注入空间自适应的扰动，以强调在最不确定区域的学习。在去模糊和超分辨率任务上的实验表明，我们的方法相较于先前的基于扩散的方法取得了更优或具有竞争力的性能，能够提供更真实的重建结果，而无需迭代优化的计算成本。