mlsanityData to 7 Oct 2026

Uncertainty quantification for ptychography using normalizing flows

arXiv 2111.00745 ↗First posted 1 Nov 2021stat.ML cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Ptychography, as an essential tool for high-resolution and nondestructive material characterization, presents a challenging large-scale nonlinear and non-convex inverse problem; however, its intrinsic photon statistics create clear opportunities for statistical-based deep learning approaches to tackle these challenges, which has been underexplored. In this work, we explore normalizing flows to obtain a surrogate for the high-dimensional posterior, which also enables the characterization of the uncertainty associated with the reconstruction: an extremely desirable capability when judging the reconstruction quality in the absence of ground truth, spotting spurious artifacts and guiding future experiments using the returned uncertainty patterns. We demonstrate the performance of the proposed method on a synthetic sample with added noise and in various physical experimental settings.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 47 references that are in the map. First posted on arXiv 1 Nov 2021.

Builds on

23 of 47 references.

References · 23

  1. 2015Variational Inference with Normalizing Flowscited by 2,161
  2. 2016Density estimation using Real NVPcited by 2,029
  3. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  4. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  5. 2014NICE: Non-linear Independent Components Estimationcited by 1,052
  6. 2018WaveGlow: A Flow-based Generative Network for Speech Synthesiscited by 308
  7. 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 290
  8. 2019Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Datacited by 261
  9. 2018Analyzing Inverse Problems with Invertible Neural Networkscited by 239
  10. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 147
  11. 2019Guided Image Generation with Conditional Invertible Neural Networkscited by 147
  12. 2019VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generationcited by 74
11 more
  1. 2019Latent Normalizing Flows for Discrete Sequencescited by 66
  2. 2020Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imagingcited by 57
  3. 2017Phase recovery and holographic image reconstruction using deep learning in neural networkscited by 54
  4. 2017Lensless computational imaging through deep learningcited by 44
  5. 2020Solving inverse problems using conditional invertible neural networkscited by 37
  6. 2014Likelihood-informed dimension reduction for nonlinear inverse problemscited by 26
  7. 2018Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identificationcited by 26
  8. 2019Reliable deep-learning-based phase imaging with uncertainty quantificationcited by 26
  9. 2021Learning by example: fast reliability-aware seismic imaging with normalizing flowscited by 10
  10. 2018Multigrid Optimization for Large-Scale Ptychographic Phase Retrievalcited by 3
  11. 2018Advanced Denoising for X-ray Ptychographycited by 2

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 1.

4 papers.

  1. 2022Uncertainty Quantification for Deep Unrolling-Based Computational Imagingcited by 10
  2. 2024Conditional score-based diffusion models for solving inverse problems in mechanicscited by 6
  3. 2025Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imagingcited by 4
  4. 2024Integrating Generative and Physics-Based Models for Ptychographic Imaging with Uncertainty Quantificationcited by 1