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

• URL: http://arxiv.org/abs/2602.11704v1
• Date: Thu, 12 Feb 2026 08:32:11 GMT
• Title: U-DAVI: Uncertainty-Aware Diffusion-Prior-Based Amortized Variational Inference for Image Reconstruction
• Authors: Ayush Varshney, Katherine L. Bouman, Berthy T. Feng,
• Abstract summary: Ill-posed imaging inverse problems remain challenging due to the ambiguity in mapping degraded observations to clean images.<n>Amortized variational inference frameworks address this inefficiency by learning a direct mapping from measurements to posteriors.<n>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.
• Score: 10.273906387994902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Robust Posterior Diffusion-based Sampling via Adaptive Guidance Scale [39.27744518020771]
  We propose an adaptive likelihood step-size strategy to guide the diffusion process for inverse-problem formulations.<n>The resulting approach, Adaptive Posterior diffusion Sampling (AdaPS), is hyper-free and improves reconstruction quality across diverse imaging tasks.
  arXiv  Detail & Related papers  (2025-11-23T14:37:59Z)
• Deep priors for satellite image restoration with accurate uncertainties [4.879530644978008]
  We propose a generic method involving a single network to restore images from several sensors.<n>VBLE-xz is a scalable method to get realistic posterior samples and accurate uncertainties.<n>SatDPIR is a compelling alternative to direct inversion methods when uncertainty is not required.
  arXiv  Detail & Related papers  (2024-12-05T12:56:03Z)
• Frequency-Guided Posterior Sampling for Diffusion-Based Image Restoration [35.35400265928447]
  We propose a simple modification to existing diffusion-based restoration methods.<n>Our approach introduces a time-varying low-pass filter in the frequency domain of the measurements.<n>We develop an adaptive curriculum for this frequency schedule based on the underlying data distribution.
  arXiv  Detail & Related papers  (2024-11-22T18:31:02Z)
• Amortized Posterior Sampling with Diffusion Prior Distillation [55.03585818289934]
  Amortized Posterior Sampling is a novel variational inference approach for efficient posterior sampling in inverse problems.<n>Our method trains a conditional flow model to minimize the divergence between the variational distribution and the posterior distribution implicitly defined by the diffusion model.<n>Unlike existing methods, our approach is unsupervised, requires no paired training data, and is applicable to both Euclidean and non-Euclidean domains.
  arXiv  Detail & Related papers  (2024-07-25T09:53:12Z)
• ReNoise: Real Image Inversion Through Iterative Noising [62.96073631599749]
  We introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations. We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models.
  arXiv  Detail & Related papers  (2024-03-21T17:52:08Z)
• Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance [52.093434664236014]
  Recent diffusion models provide a promising zero-shot solution to noisy linear inverse problems without retraining for specific inverse problems. Inspired by this finding, we propose to improve recent methods by using more principled covariance determined by maximum likelihood estimation.
  arXiv  Detail & Related papers  (2024-02-03T13:35:39Z)
• On the Quantification of Image Reconstruction Uncertainty without Training Data [5.057039869893053]
  We propose a deep variational framework that leverages a deep generative model to learn an approximate posterior distribution. We parameterize the target posterior using a flow-based model and minimize their Kullback-Leibler (KL) divergence to achieve accurate uncertainty estimation. Our results indicate that our method provides reliable and high-quality image reconstruction with robust uncertainty estimation.
  arXiv  Detail & Related papers  (2023-11-16T07:46:47Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion.<n>This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement.<n>We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• Exploiting Diffusion Prior for Real-World Image Super-Resolution [75.5898357277047]
  We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution. By employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model.
  arXiv  Detail & Related papers  (2023-05-11T17:55:25Z)
• DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency [24.5360032541275]
  Diffusion models have established new state of the art in a multitude of computer vision tasks, including image restoration. We propose a novel framework for inverse problem solving, namely we assume that the observation comes from a degradation process that gradually degrades and noises the original clean image. Our technique maintains consistency with the original measurement throughout the reverse process, and allows for great flexibility in trading off perceptual quality for improved distortion metrics and sampling speedup via early-stopping.
  arXiv  Detail & Related papers  (2023-03-25T04:37:20Z)
• DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection [80.20339155618612]
  DiffusionAD is a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network.<n>A rapid one-step denoising paradigm achieves hundreds of times acceleration while preserving comparable reconstruction quality.<n>Considering the diversity in the manifestation of anomalies, we propose a norm-guided paradigm to integrate the benefits of multiple noise scales.
  arXiv  Detail & Related papers  (2023-03-15T16:14:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.