Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction
Not on the map. Why?
Abstract (arXiv)
Reconstructing an image from noisy and incomplete measurements is a central task in several image processing applications. In recent years, state-of-the-art reconstruction methods have been developed based on recent advances in deep learning. Especially for highly underdetermined problems, maintaining data consistency is a key goal. This can be achieved either by iterative network architectures or by a subsequent projection of the network reconstruction. However, for such approaches to be used in safety-critical domains such as medical imaging, the network reconstruction should not only provide the user with a reconstructed image, but also with some level of confidence in the reconstruction. In order to meet these two key requirements, this paper combines deep null-space networks with uncertainty quantification. Evaluation of the proposed method includes image reconstruction from undersampled Radon measurements on a toy CT dataset and accelerated MRI reconstruction on the fastMRI dataset. This work is the first approach to solving inverse problems that additionally models data-dependent uncertainty by estimating an input-dependent scale map, providing a robust assessment of reconstruction quality.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 26 of its 56 references that are in the map. First posted on arXiv 14 Apr 2023.
Builds on
26 of 56 references.
References · 26
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
- 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
- 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
- 2017Generative Adversarial Networks: An Overviewcited by 590
- 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
- 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 433
- 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 342
- 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 291
- 2017Solving ill-posed inverse problems using iterative deep neural networkscited by 145
- 2019Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Surveycited by 93
14 more
- 2017Deep learning for undersampled MRI reconstructioncited by 91
- 2018NETT: Solving Inverse Problems with Deep Neural Networkscited by 89
- 2017CNN-Based Projected Gradient Descent for Consistent Image Reconstructioncited by 77
- 2017A Deep Cascade of Convolutional Neural Networks for MR Image Reconstructioncited by 74
- 2018Deep Bayesian Inversioncited by 61
- 2020Solving Inverse Problems With Deep Neural Networks -- Robustness Included?cited by 58
- 2021Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstructioncited by 34
- 2017DR2-Net: Deep Residual Reconstruction Network for Image Compressive Sensingcited by 30
- 2016Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysiscited by 22
- 2021Robustness via Uncertainty-aware Cycle Consistencycited by 16
- 2019The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty predictioncited by 10
- 2020Regularization of Inverse Problems by Neural Networkscited by 6
- 2020Deep Structure Learning using Feature Extraction in Trained Projection Spacecited by 2
- 2022Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networkscited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.