Data to 14 Oct 2026

Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods

arXiv 2003.04989 ↗First posted 10 Mar 2020eess.IV cs.CV cs.LGcited by 56

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Abstract (arXiv)

In this work, we investigate the application of deep learning methods for computed tomography in the context of having a low-data regime. As motivation, we review some of the existing approaches and obtain quantitative results after training them with different amounts of data. We find that the learned primal-dual has an outstanding performance in terms of reconstruction quality and data efficiency. However, in general, end-to-end learned methods have two issues: a) lack of classical guarantees in inverse problems and b) lack of generalization when not trained with enough data. To overcome these issues, we bring in the deep image prior approach in combination with classical regularization. The proposed methods improve the state-of-the-art results in the low data-regime.

Key facts. Cited by 56 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Mar 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 10; 2022: 16; 2023: 10; 2024: 9; 2025: 6; 2026: 5.

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2022DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstructioncited by 74
2021Early Stopping for Deep Image Priorcited by 44
2022PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularizationcited by 22
2024Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problemscited by 21
2022Sampling-based inference for large linear models, with application to linearised Laplacecited by 18
2023NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matchingcited by 18
2024DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstructioncited by 17
2021An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstructioncited by 16
2023Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstructioncited by 15
2022NeAT: Neural Adaptive Tomographycited by 11
2021Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problemscited by 10
2022WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolutioncited by 10

38 more

2022Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Priorcited by 10
2022Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Priorcited by 7
2021Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyondcited by 5
2022Convergent Data-driven Regularizations for CT Reconstructioncited by 5
2024Variational Zero-shot Multispectral Pansharpeningcited by 5
2021Unsupervised Knowledge-Transfer for Learned Image Reconstructioncited by 4
2025Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomographycited by 4
2021Posterior Temperature Optimization in Variational Inference for Inverse Problemscited by 3
2022Posterior temperature optimized Bayesian models for inverse problems in medical imagingcited by 3
2022Clean Implicit 3D Structure from Noisy 2D STEM Imagescited by 3
2022LRIP-Net: Low-Resolution Image Prior based Network for Limited-Angle CT Reconstructioncited by 3
2023Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problemscited by 3
2026DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstructioncited by 3
2022Deep Learning for Material Decomposition in Photon-Counting CTcited by 2
2022Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation timecited by 2
2023SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstructioncited by 2
2023Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuitscited by 2
2024Diff-INR: Generative Regularization for Electrical Impedance Tomographycited by 2
20213D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutionscited by 1
2021Deep learning based dictionary learning and tomographic image reconstructioncited by 1
2022A Dataset-free Deep learning Method for Low-Dose CT Image Reconstructioncited by 1
2022Residual Back Projection With Untrained Neural Networkscited by 1
2022Unsupervised denoising for sparse multi-spectral computed tomographycited by 1
2023Image Reconstruction via Deep Image Prior Subspacescited by 1
2024Low-resolution Prior Equilibrium Network for CT Reconstructioncited by 1
2024Evaluating Adversarial Robustness of Low dose CT Recoverycited by 1
2024SDIP: Self-Reinforcement Deep Image Prior Framework for Image Processingcited by 1
2024Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Modelscited by 1
2025Data-Efficient Limited-Angle CT Using Deep Priors and Regularizationcited by 1
2025Physics-Driven Neural Compensation For Electrical Impedance Tomographycited by 1
2026GSNR: Graph Smooth Null-Space Representation for Inverse Problemscited by 1
2021An Adversarial Learning Based Approach for Unknown View Tomographic Reconstructioncited by 0
2021Deformable image registration with deep network priors: a study on longitudinal PET imagescited by 0
2023Conditioning Generative Latent Optimization for Sparse-View CT Image Reconstructioncited by 0
2023Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN samplercited by 0
2023Single-shot Tomography of Discrete Dynamic Objectscited by 0
2023Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learningcited by 0
2024Deep Guess acceleration for explainable image reconstruction in sparse-view CTcited by 0

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