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This paper introduces U-DAVI, an uncertainty-aware amortized variational inference framework for image reconstruction that leverages diffusion priors. By injecting spatially adaptive perturbations to measurements during training, guided by uncertainty estimates, U-DAVI focuses learning on uncertain regions, improving reconstruction quality. Experiments on deblurring and super-resolution tasks demonstrate that U-DAVI achieves competitive or superior performance compared to existing diffusion-based methods, while maintaining computational efficiency.
Achieve faster, more realistic image reconstructions in ill-posed problems by training a diffusion-based amortized variational inference model with uncertainty-guided perturbations.
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.