mlsanityData to 7 Oct 2026

Uncertainty Quantification for Deep Unrolling-Based Computational Imaging

arXiv 2207.00698 ↗First posted 2 Jul 2022eess.IV cs.LG eess.SPcited by 10

Not on the map. Why?

Abstract (arXiv)

Deep unrolling is an emerging deep learning-based image reconstruction methodology that bridges the gap between model-based and purely deep learning-based image reconstruction methods. Although deep unrolling methods achieve state-of-the-art performance for imaging problems and allow the incorporation of the observation model into the reconstruction process, they do not provide any uncertainty information about the reconstructed image, which severely limits their use in practice, especially for safety-critical imaging applications. In this paper, we propose a learning-based image reconstruction framework that incorporates the observation model into the reconstruction task and that is capable of quantifying epistemic and aleatoric uncertainties, based on deep unrolling and Bayesian neural networks. We demonstrate the uncertainty characterization capability of the proposed framework on magnetic resonance imaging and computed tomography reconstruction problems. We investigate the characteristics of the epistemic and aleatoric uncertainty information provided by the proposed framework to motivate future research on utilizing uncertainty information to develop more accurate, robust, trustworthy, uncertainty-aware, learning-based image reconstruction and analysis methods for imaging problems. We show that the proposed framework can provide uncertainty information while achieving comparable reconstruction performance to state-of-the-art deep unrolling methods.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 41 of its 69 references that are in the map. First posted on arXiv 2 Jul 2022.

Builds on

41 of 69 references.

References · 41

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  4. 2013Auto-Encoding Variational Bayescited by 3,980
  5. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  6. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  7. 2014Conditional Generative Adversarial Netscited by 2,152
  8. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  9. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
  10. 2017Deep Image Priorcited by 1,097
  11. 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 467
  12. 2018Accurate Uncertainties for Deep Learning Using Calibrated Regressioncited by 417
29 more
  1. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 384
  2. 2020Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Userscited by 336
  3. 2017Learned Primal-dual Reconstructioncited by 251
  4. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
  5. 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 231
  6. 2016Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challengecited by 198
  7. 2019Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Visioncited by 182
  8. 2015Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inferencecited by 159
  9. 2015Modelling Uncertainty in Deep Learning for Camera Relocalizationcited by 133
  10. 2020Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imagingcited by 57
  11. 2021Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantificationcited by 57
  12. 2016Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networkscited by 49
  13. 2019Uncertainty Quantification in Deep MRI Reconstructioncited by 49
  14. 2018Neural Proximal Gradient Descent for Compressive Imagingcited by 46
  15. 2020TorchRadon: Fast Differentiable Routines for Computed Tomographycited by 44
  16. 2017Unrolled Optimization with Deep Priorscited by 40
  17. 2019Reliable deep-learning-based phase imaging with uncertainty quantificationcited by 26
  18. 2018Scalable Bayesian uncertainty quantification in imaging inverse problems via convex optimizationcited by 22
  19. 2019Uncertainty Quantification with Generative Modelscited by 17
  20. 2017Uncertainty quantification for radio interferometric imaging: I. proximal MCMC methodscited by 13
  21. 2018Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recoverycited by 11
  22. 2021Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurementscited by 11
  23. 2019Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancementcited by 9
  24. 2020A deep-learning based Bayesian approach to seismic imaging and uncertainty quantificationcited by 9
  25. 2019Learned imaging with constraints and uncertainty quantificationcited by 8
  26. 2017Uncertainty quantification for radio interferometric imaging: II. MAP estimationcited by 5
  27. 2021Uncertainty quantification for ptychography using normalizing flowscited by 4
  28. 2020SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk Estimatecited by 3
  29. 2021Deep-learning-driven Reliable Single-pixel Imaging with Uncertainty Approximationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 5; 2026: 1.

10 papers.

  1. 2024Estimating Epistemic and Aleatoric Uncertainty with a Single Modelcited by 52
  2. 2024Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Predictioncited by 8
  3. 2024Graph Structure Learning with Interpretable Bayesian Neural Networkscited by 4
  4. 2025Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imagingcited by 4
  5. 2024Non-Asymptotic Uncertainty Quantification in High-Dimensional Learningcited by 2
  6. 2025Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problemscited by 2
  7. 2025Disentangling Uncertainties by Learning Compressed Data Representationcited by 1
  8. 2026When Variance Is Not an Error Map: Calibrated Uncertainty for Radiative Gaussian Splatting in Sparse-View CTcited by 1
  9. 2025Uncertainty Estimation for Trust Attribution to Speed-of-Sound Reconstruction with Variational Networkscited by 0
  10. 2025UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomographycited by 0