mlsanityData to 7 Oct 2026

Deformation-Compensated Learning for Image Reconstruction without Ground Truth

arXiv 2107.05533 ↗First posted 12 Jul 2021eess.IV cs.CVcited by 9

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Abstract (arXiv)

Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets. Recent work on Noise2Noise (N2N) has shown the potential of using multiple noisy measurements of the same object as an alternative to having a ground-truth. However, existing N2N-based methods are not suitable for learning from the measurements of an object undergoing nonrigid deformation. This paper addresses this issue by proposing the deformation-compensated learning (DeCoLearn) method for training deep reconstruction networks by compensating for object deformations. A key component of DeCoLearn is a deep registration module, which is jointly trained with the deep reconstruction network without any ground-truth supervision. We validate DeCoLearn on both simulated and experimentally collected magnetic resonance imaging (MRI) data and show that it significantly improves imaging quality.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 40 of its 89 references that are in the map. First posted on arXiv 12 Jul 2021.

Builds on

40 of 89 references.

References · 40

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28 more
  1. 2016The Little Engine that Could: Regularization by Denoising (RED)cited by 303
  2. 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 290
  3. 2019Noise2Self: Blind Denoising by Self-Supervisioncited by 274
  4. 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 231
  5. 2019High-Quality Self-Supervised Deep Image Denoisingcited by 161
  6. 2018A Deep Learning Framework for Unsupervised Affine and Deformable Image Registrationcited by 160
  7. 2019Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernelscited by 112
  8. 2019Deep Learning in Medical Image Registration: A Reviewcited by 98
  9. 2019DeepRED: Deep Image Prior Powered by REDcited by 84
  10. 2017A Review of Convolutional Neural Networks for Inverse Problems in Imagingcited by 81
  11. 2019Deep Learning Methods for Parallel Magnetic Resonance Image Reconstructioncited by 81
  12. 2015Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolationcited by 78
  13. 2019Plug and play methods for magnetic resonance imaging (long version)cited by 75
  14. 2019Time-Dependent Deep Image Prior for Dynamic MRIcited by 70
  15. 2018Image Restoration using Total Variation Regularized Deep Image Priorcited by 65
  16. 2019RARE: Image Reconstruction using Deep Priors Learned without Ground Truthcited by 64
  17. 2021Zero-Shot Self-Supervised Learning for MRI Reconstructioncited by 53
  18. 2019Probabilistic Noise2Void: Unsupervised Content-Aware Denoisingcited by 51
  19. 2018Training Deep Learning Based Denoisers without Ground Truth Datacited by 43
  20. 2018Model-blind Video Denoising Via Frame-to-frame Trainingcited by 34
  21. 2015On learning optimized reaction diffusion processes for effective image restorationcited by 25
  22. 2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
  23. 2019Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Datacited by 17
  24. 2021Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imagingcited by 15
  25. 2018Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy Datacited by 13
  26. 2017ssEMnet: Serial-section Electron Microscopy Image Registration using a Spatial Transformer Network with Learned Featurescited by 7
  27. 2018Regularized Fourier Ptychography using an Online Plug-and-Play Algorithmcited by 7
  28. 2020Deep Image Reconstruction using Unregistered Measurements without Groundtruthcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 2; 2025: 1; 2026: 1.

9 papers.

  1. 2022A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noisecited by 29
  2. 2023Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Reviewcited by 18
  3. 2025Benchmarking Self-Supervised Learning Methods for Accelerated MRI Reconstructioncited by 4
  4. 2024Fully Unsupervised Dynamic MRI Reconstruction via Diffeo-Temporal Equivariancecited by 3
  5. 2023Efficient Model-Based Deep Learning via Network Pruning and Fine-Tuningcited by 2
  6. 2022Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guaranteescited by 1
  7. 2026A Unified Framework for Multimodal Image Reconstruction and Synthesis using Denoising Diffusion Modelscited by 1
  8. 2023Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Datacited by 0
  9. 2024Investigating Self-Supervised Image Denoising with Denaturationcited by 0