Uncertainty quantification for ptychography using normalizing flows

• URL: http://arxiv.org/abs/2111.00745v1
• Date: Mon, 1 Nov 2021 07:46:22 GMT
• Title: Uncertainty quantification for ptychography using normalizing flows
• Authors: Agnimitra Dasgupta and Zichao Wendy Di
• Abstract summary: Ptychography presents a challenging large-scale nonlinear and non-dimensional inverse problem. Its intrinsic photo statistics create clear opportunities for statistical-based deep learning approaches to tackle these challenges. In this work, we explore flows to obtain a surrogate high-dimensional posterior, which enables characterization of the uncertainty associated with the reconstruction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes [1.534667887016089]
  We leverage training data to discriminatively learn particle-based representations of uncertainty in latent object states. Our approach achieves dramatic improvements in accuracy, while also showing much greater stability across multiple training runs.
  arXiv  Detail & Related papers  (2024-04-12T19:33:52Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• Towards stable real-world equation discovery with assessing differentiating quality influence [52.2980614912553]
  We propose alternatives to the commonly used finite differences-based method. We evaluate these methods in terms of applicability to problems, similar to the real ones, and their ability to ensure the convergence of equation discovery algorithms.
  arXiv  Detail & Related papers  (2023-11-09T23:32:06Z)
• Equivariant Bootstrapping for Uncertainty Quantification in Imaging Inverse Problems [0.24475591916185502]
  We present a new uncertainty quantification methodology based on an equivariant formulation of the parametric bootstrap algorithm. The proposed methodology is general and can be easily applied with any image reconstruction technique. We demonstrate the proposed approach with a series of numerical experiments and through comparisons with alternative uncertainty quantification strategies.
  arXiv  Detail & Related papers  (2023-10-18T09:43:15Z)
• Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations [69.25683256447044]
  We introduce an attack approach designed to effectively degrade the reconstruction quality of Learned Image Compression (LIC) We generate adversarial examples by introducing a Frobenius norm-based loss function to maximize the discrepancy between original images and reconstructed adversarial examples. Experiments conducted on the Kodak dataset using various LIC models demonstrate effectiveness.
  arXiv  Detail & Related papers  (2023-06-01T20:21:05Z)
• DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection [89.49600182243306]
  We reformulate the reconstruction process using a diffusion model into a noise-to-norm paradigm. We propose a rapid one-step denoising paradigm, significantly faster than the traditional iterative denoising in diffusion models. The segmentation sub-network predicts pixel-level anomaly scores using the input image and its anomaly-free restoration.
  arXiv  Detail & Related papers  (2023-03-15T16:14:06Z)
• Simultaneous Reconstruction and Uncertainty Quantification for Tomography [0.0]
  In the absence of ground truth, quantifying the solution quality is highly desirable but under-explored. In this work, we address this challenge through Gaussian process modeling to flexibly and explicitly incorporate prior knowledge of sample features and experimental noises. Our proposed method yields not only comparable reconstruction to existing practical reconstruction methods but also an efficient way of quantifying solution uncertainties.
  arXiv  Detail & Related papers  (2021-03-29T18:16:57Z)
• Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction [63.202627467245584]
  We introduce a Bayesian variational framework to quantify the model-immanent (epistemic) uncertainty. We demonstrate that our approach yields competitive results for undersampled MRI reconstruction.
  arXiv  Detail & Related papers  (2021-02-12T18:08:14Z)
• Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction [5.129343375966527]
  We propose a scalable and efficient framework to simultaneously quantify aleatoric and epistemic uncertainties in learned iterative image reconstruction. We show that our method exhibits competitive performance against conventional benchmarks for computed tomography with both sparse view and limited angle data.
  arXiv  Detail & Related papers  (2020-11-17T04:12:52Z)
• Total Deep Variation for Linear Inverse Problems [71.90933869570914]
  We propose a novel learnable general-purpose regularizer exploiting recent architectural design patterns from deep learning. We show state-of-the-art performance for classical image restoration and medical image reconstruction problems.
  arXiv  Detail & Related papers  (2020-01-14T19:01:50Z)
• A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification [0.4588028371034407]
  Uncertainty is essential when dealing with ill-conditioned inverse problems. It is often not possible to formulate a prior distribution that precisely encodes our prior knowledge about the unknown. We propose to use the functional form of a randomly convolutional neural network as an implicit structured prior.
  arXiv  Detail & Related papers  (2020-01-13T23:46:18Z)

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.