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Found 10 papers, 3 papers with code
Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learning
In our experiments, we study combinations of supervised deep network reconstructors and MBIR solver with learned sparse representation-based priors or analytical priors.
Reinforcement Learning for Sampling on Temporal Medical Imaging Sequences
Accelerated magnetic resonance imaging resorts to either Fourier-domain subsampling or better reconstruction algorithms to deal with fewer measurements while still generating medical images of high quality.
Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction
Recent application of deep learning methods for image reconstruction provides a successful data-driven approach to addressing the challenges when reconstructing images with measurement undersampling or various types of noise.
Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction
In this study, we propose a network-structured sparsifying transform learning approach for X-ray computed tomography (CT), which we refer to as multi-layer clustering-based residual sparsifying transform (MCST) learning.
Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI
There is much recent interest in techniques to accelerate the data acquisition process in MRI by acquiring limited measurements.
Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic Tomography
Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications.
Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond
Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements.
Stochastic Gradient Langevin Dynamics with Variance Reduction
Stochastic gradient Langevin dynamics (SGLD) has gained the attention of optimization researchers due to its global optimization properties.
Spectral estimation from simulations via sketching
Sketching is a stochastic dimension reduction method that preserves geometric structures of data and has applications in high-dimensional regression, low rank approximation and graph sparsification.
Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions
We consider the problem of finding local minimizers in non-convex and non-smooth optimization.
