ArticleIEEE transactions on pattern analysis and machine intelligence2023
Momentum-Net: Fast and Convergent Iterative Neural Network for Inverse Problems.
Article in IEEE transactions on pattern analysis and machine intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A Framework for Guiding DDPM-Based Reconstruction of Damaged CT Projections Using Traditional Methods.Journal of imaging informatics in medicine · 2026Article
- An interpretable cascaded residual iterative network for sparse-view spectral CT imaging.Quantitative imaging in medicine and surgery · 2026Article
- Variability Regularized Feature Selection (VaRFS) for optimal identification of robust and discriminable features from medical imaging.Npj imaging · 2026Article
- Application of AIIR algorithm for quality improvement and noise reduction in pediatric abdominal contrast-enhanced CT.Frontiers in radiology · 2026Article
- Self-supervised learning for CT image denoising and reconstruction: a review.Biomedical engineering letters · 2024Review
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- MB-DECTNet: a model-based unrolling network for accurate 3D dual-energy CT reconstruction from clinically acquired helical scans.Physics in medicine and biology · 2023Article
- Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey.IEEE signal processing magazine · 2023Article
- Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging.Sensors (Basel, Switzerland) · 2022Article
- NeRP: Implicit Neural Representation Learning With Prior Embedding for Sparsely Sampled Image Reconstruction.IEEE transactions on neural networks and learning systems · 2022Article
- Stabilizing deep tomographic reconstruction: Part B. Convergence analysis and adversarial attacks.Patterns (New York, N.Y.) · 2022Article
- A New Framework of Designing Iterative Techniques for Image Deblurring.Pattern recognition · 2022Article
- Reconstruction of three-dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning.Medical physics · 2022Article
- Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems.Entropy (Basel, Switzerland) · 2021Article
- Low-dose CT reconstruction with Noise2Noise network and testing-time fine-tuning.Medical physics · 2021Article
- DRONE: Dual-Domain Residual-based Optimization NEtwork for Sparse-View CT Reconstruction.IEEE transactions on medical imaging · 2021Article
- Accurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS).Medical physics · 2021Article
- Neural network based 3D tracking with a graphene transparent focal stack imaging system.Nature communications · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision. INNs combine regression NNs and an iterative model-based image reconstruction (MBIR) algorithm, often leading to both good generalization capability and outperforming reconstruction quality over existing MBIR optimization models. This paper proposes the first fast and convergent INN architecture, Momentum-Net, by generalizing a block-wise MBIR algorithm that uses momentum and majorizers with regression NNs. For fast MBIR, Momentum-Net uses momentum terms in extrapolation modules, and noniterative MBIR modules at each iteration by using majorizers, where each iteration of Momentum-Net consists of three core modules: image refining, extrapolation, and MBIR. Momentum-Net guarantees convergence to a fixed-point for general differentiable (non)convex MBIR functions (or data-fit terms) and convex feasible sets, under two asymptomatic conditions. To consider data-fit variations across training and testing samples, we also propose a regularization parameter selection scheme based on the "spectral spread" of majorization matrices. Numerical experiments for light-field photography using a focal stack and sparse-view computational tomography demonstrate that, given identical regression NN architectures, Momentum-Net significantly improves MBIR speed and accuracy over several existing INNs; it significantly improves reconstruction quality compared to a state-of-the-art MBIR method in each application.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.