U-DAVI: Uncertainty-Aware Diffusion-Prior-Based Amortized Variational Inference for Image Reconstruction

TL;DR

This paper introduces U-DAVI, an uncertainty-aware amortized variational inference method that improves image reconstruction quality by focusing learning on uncertain regions, achieving competitive results with reduced computational cost.

Contribution

The paper proposes a novel uncertainty-guided perturbation technique within an amortized inference framework to enhance image reconstruction quality.

Findings

Outperforms previous diffusion-based methods in deblurring and super-resolution.

Produces more realistic reconstructions without iterative refinement.

Achieves faster inference by avoiding per-instance optimization.

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.