Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging

• URL: http://arxiv.org/abs/2010.14462v2
• Date: Thu, 17 Dec 2020 06:13:58 GMT
• Title: Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging
• Authors: He Sun, Katherine L. Bouman
• Abstract summary: We propose a variational deep probabilistic imaging approach to quantify reconstruction uncertainty. Deep Probabilistic Imaging employs an untrained deep generative model to estimate a posterior distribution of an unobserved image.
• Score: 11.677576854233394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computational image reconstruction algorithms generally produce a single image without any measure of uncertainty or confidence. Regularized Maximum Likelihood (RML) and feed-forward deep learning approaches for inverse problems typically focus on recovering a point estimate. This is a serious limitation when working with underdetermined imaging systems, where it is conceivable that multiple image modes would be consistent with the measured data. Characterizing the space of probable images that explain the observational data is therefore crucial. In this paper, we propose a variational deep probabilistic imaging approach to quantify reconstruction uncertainty. Deep Probabilistic Imaging (DPI) employs an untrained deep generative model to estimate a posterior distribution of an unobserved image. This approach does not require any training data; instead, it optimizes the weights of a neural network to generate image samples that fit a particular measurement dataset. Once the network weights have been learned, the posterior distribution can be efficiently sampled. We demonstrate this approach in the context of interferometric radio imaging, which is used for black hole imaging with the Event Horizon Telescope, and compressed sensing Magnetic Resonance Imaging (MRI).

Related papers

• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• NeRF Solves Undersampled MRI Reconstruction [1.3597551064547502]
  This article presents a novel undersampled magnetic resonance imaging (MRI) technique that leverages the concept of Neural Radiance Field (NeRF) With radial undersampling, the corresponding imaging problem can be reformulated into an image modeling task from sparse-view rendered data. A multi-layer perceptron, which is designed to output an image intensity from a spatial coordinate, learns the MR physics-driven rendering relation between given measurement data and desired image.
  arXiv  Detail & Related papers  (2024-02-20T18:37:42Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Uncertainty Quantification via Neural Posterior Principal Components [26.26693707762823]
  Uncertainty quantification is crucial for the deployment of image restoration models in safety-critical domains. We present a method for predicting the PCs of the posterior distribution for any input image, in a single forward pass of a neural network. Our method reliably conveys instance-adaptive uncertainty directions, achieving uncertainty quantification comparable with posterior samplers.
  arXiv  Detail & Related papers  (2023-09-27T09:51:29Z)
• Stable Deep MRI Reconstruction using Generative Priors [13.400444194036101]
  We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only. The results demonstrate competitive performance, on par with state-of-the-art end-to-end deep learning methods.
  arXiv  Detail & Related papers  (2022-10-25T08:34:29Z)
• Compressive Ptychography using Deep Image and Generative Priors [9.658250977094562]
  Ptychography is a well-established coherent diffraction imaging technique that enables non-invasive imaging of samples at a nanometer scale. One major limitation of ptychography is the long data acquisition time due to mechanical scanning of the sample. We propose a generative model combining deep image priors with deep generative priors.
  arXiv  Detail & Related papers  (2022-05-05T02:18:26Z)
• Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems [10.115302976900445]
  Tomographic imaging is in general an ill-posed inverse problem. We propose a new empirical sampling method that computes multiple solutions of a tomographic inverse problem.
  arXiv  Detail & Related papers  (2022-02-10T20:27:31Z)
• Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging [88.20869695803631]
  We show how to derive uncertainty intervals around each pixel that are guaranteed to contain the true value. We evaluate our procedure on three image-to-image regression tasks.
  arXiv  Detail & Related papers  (2022-02-10T18:59:56Z)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images [70.13735569016752]
  We investigate the robustness of deep learning in CT image reconstruction by showing false negative and false positive lesion cases. We propose a data consistent reconstruction (DCR) method to improve their image quality, which combines the advantages of compressed sensing and deep learning. The efficacy of the proposed method is demonstrated in cone-beam CT with truncated data, limited-angle data and sparse-view data, respectively.
  arXiv  Detail & Related papers  (2020-05-20T13:30:49Z)

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.