mlsanityData to 14 Oct 2026

NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction

arXiv 2108.10991 ↗First posted 24 Aug 2021eess.IV cs.CVcited by 146

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Abstract (arXiv)

Image reconstruction is an inverse problem that solves for a computational image based on sampled sensor measurement. Sparsely sampled image reconstruction poses addition challenges due to limited measurements. In this work, we propose an implicit Neural Representation learning methodology with Prior embedding (NeRP) to reconstruct a computational image from sparsely sampled measurements. The method differs fundamentally from previous deep learning-based image reconstruction approaches in that NeRP exploits the internal information in an image prior, and the physics of the sparsely sampled measurements to produce a representation of the unknown subject. No large-scale data is required to train the NeRP except for a prior image and sparsely sampled measurements. In addition, we demonstrate that NeRP is a general methodology that generalizes to different imaging modalities such as CT and MRI. We also show that NeRP can robustly capture the subtle yet significant image changes required for assessing tumor progression.

Key facts. Cited by 146 papers in the map (data through 14 Oct 2026). Builds on 13 of its 41 references that are in the map. First posted on arXiv 24 Aug 2021.

Builds on

13 of 41 references.

References · 13

  1. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  2. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  3. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,232
  4. 2017Deep Image Priorcited by 1,098
  5. 2019Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationscited by 864
  6. 2017Image reconstruction by domain transform manifold learningcited by 232
  7. 2019On instabilities of deep learning in image reconstruction - Does AI come at a cost?cited by 212
  8. 2018"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priorscited by 114
  9. 2019X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networkscited by 85
  10. 2019DuDoNet: Dual Domain Network for CT Metal Artifact Reductioncited by 68
  11. 2019Momentum-Net: Fast and convergent iterative neural network for inverse problemscited by 38
  12. 2021CoIL: Coordinate-based Internal Learning for Imaging Inverse Problemscited by 33
1 more
  1. 2020Deep Low-rank plus Sparse Network for Dynamic MR Imagingcited by 32

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 9; 2023: 26; 2024: 42; 2025: 42; 2026: 26.

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  1. 2021Neural Fields in Visual Computing and Beyondcited by 540
  2. 2023Implicit Neural Representation in Medical Imaging: A Comparative Surveycited by 90
  3. 2024R2-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstructioncited by 67
  4. 2023Spatial Functa: Scaling Functa to ImageNet Classification and Generationcited by 57
  5. 2023Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstructioncited by 42
  6. 2022Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstructioncited by 36
  7. 2022Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imagingcited by 35
  8. 2024A survey of synthetic data augmentation methods in computer visioncited by 34
  9. 2023Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representationscited by 32
  10. 2022Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomographycited by 31
  11. 2023INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddingscited by 31
  12. 2023CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolutioncited by 28
38 more
  1. 20233DGR-CT: Sparse-View CT Reconstruction with a 3D Gaussian Representationcited by 25
  2. 2024A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directionscited by 25
  3. 2023ZeroRF: Fast Sparse View 360deg Reconstruction with Zero Pretrainingcited by 24
  4. 2023IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRIcited by 23
  5. 2022Medical Image Registration via Neural Fieldscited by 19
  6. 2024Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstructioncited by 18
  7. 2024DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstructioncited by 17
  8. 2023Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstructioncited by 15
  9. 2024C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstructioncited by 15
  10. 2024EF-3DGS: Event-Aided Free-Trajectory 3D Gaussian Splattingcited by 15
  11. 2024Learning Transferable Features for Implicit Neural Representationscited by 14
  12. 2023Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstructioncited by 12
  13. 2024Volumetrically Consistent 3D Gaussian Rasterizationcited by 12
  14. 2022Learning Cross-Video Neural Representations for High-Quality Frame Interpolationcited by 11
  15. 2023Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reductioncited by 9
  16. 2024Implicit Neural Representations for Robust Joint Sparse-View CT Reconstructioncited by 9
  17. 2023Unsupervised reconstruction of accelerated cardiac cine MRI using Neural Fieldscited by 8
  18. 2023CT Reconstruction from Few Planar X-rays with Application towards Low-resource Radiotherapycited by 8
  19. 2024DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rayscited by 8
  20. 2025Explorable INR: An Implicit Neural Representation for Ensemble Simulation Enabling Efficient Spatial and Parameter Explorationcited by 8
  21. 20243DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representationcited by 7
  22. 2023Implicit Neural Representations for Modeling of Abdominal Aortic Aneurysm Progressioncited by 6
  23. 2023MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Imagescited by 6
  24. 2024MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy Projectionscited by 6
  25. 2025X-LRM: X-ray Large Reconstruction Model for Extremely Sparse-View Computed Tomography Recovery in One Secondcited by 6
  26. 2025X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomographycited by 6
  27. 2022Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Fieldcited by 5
  28. 2023TiAVox: Time-aware Attenuation Voxels for Sparse-view 4D DSA Reconstructioncited by 5
  29. 2023Continuous 3D Myocardial Motion Tracking via Echocardiographycited by 5
  30. 2024Highly Accelerated MRI via Implicit Neural Representation Guided Posterior Sampling of Diffusion Modelscited by 5
  31. 2025Distilling Dataset into Neural Fieldcited by 5
  32. 2025DeepSparse: A Foundation Model for Sparse-View CBCT Reconstructioncited by 5
  33. 2023Generative modeling of living cells with SO(3)-equivariant implicit neural representationscited by 4
  34. 2023SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddingscited by 4
  35. 2024INFusion: Diffusion Regularized Implicit Neural Representations for 2D and 3D accelerated MRI reconstructioncited by 4
  36. 2024Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representationcited by 4
  37. 2024Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCTcited by 4
  38. 2025PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRIcited by 4

50 most cited of 146 All 146 (JSON)