Search Results for author:
Found 27 papers, 6 papers with code
Inverse Problems with Learned Forward Operators
The first one is completely agnostic to the forward operator and learns its restriction to the subspace spanned by the training data.
Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches
A suite of performance metrics is employed to assess the efficacy of these methods.
Convergent regularization in inverse problems and linear plug-and-play denoisers
While a significant amount of research has gone into establishing the convergence of the PnP iteration for different regularity conditions on the denoisers, not much is known about the asymptotic properties of the converged solution as the noise level in the measurement tends to zero, i. e., whether PnP methods are provably convergent regularization schemes under reasonable assumptions on the denoiser.
Domain independent post-processing with graph U-nets: Applications to Electrical Impedance Tomographic Imaging
We demonstrate effectiveness and flexibility of the graph U-Net for improving reconstructions from electrical impedance tomographic (EIT) measurements, a nonlinear and highly ill-posed inverse problem.
Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications
Compressed sensing multi-user detection (CS-MUD) algorithms play a key role in optimizing grant-free (GF) non-orthogonal multiple access (NOMA) for massive machine-type communications (mMTC).
Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography
In this work we advance the methodological and theoretical basis for model corrections in learned image reconstructions by embedding the model correction in a learned primal-dual framework.
Unsupervised denoising for sparse multi-spectral computed tomography
However, these issues are especially exacerbated when sparse imaging scenarios are encountered due to a significant reduction in photon counts.}
Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs
Thus, here we propose the use of a Dimension reduced Kalman Filter to accumulate information between slices and allow for sufficiently accurate reconstructions for further assessment of the object.
Learned reconstruction methods with convergence guarantees
In recent years, deep learning has achieved remarkable empirical success for image reconstruction.
An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks.
Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data
Many interventional surgical procedures rely on medical imaging to visualise and track instruments.
Unsupervised Knowledge-Transfer for Learned Image Reconstruction
Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities.
Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems
This gives rise to the proposed iterative Graph Convolutional Newton-type Method (GCNM), which includes the forward model in the solution of the inverse problem, while all updates are directly computed by the network on the problem specific mesh.
An efficient Quasi-Newton method for nonlinear inverse problems via learned singular values
Here we present a highly efficient data-driven Quasi-Newton method applicable to nonlinear inverse problems.
Machine Learning in Magnetic Resonance Imaging: Image Reconstruction
Following the success of machine learning in a wide range of imaging tasks, there has been a recent explosion in the use of machine learning in the field of MRI image reconstruction.
Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction
Image reconstruction methods based on deep neural networks have shown outstanding performance, equalling or exceeding the state-of-the-art results of conventional approaches, but often do not provide uncertainty information about the reconstruction.
Deep Learning in Photoacoustic Tomography: Current approaches and future directions
The need for rapid image formation and the practical restrictions on data acquisition that arise from the constraints of a clinical workflow are presenting new image reconstruction challenges.
On the unreasonable effectiveness of CNNs
Deep learning methods using convolutional neural networks (CNN) have been successfully applied to virtually all imaging problems, and particularly in image reconstruction tasks with ill-posed and complicated imaging models.
Blind hierarchical deconvolution
Deconvolution is a fundamental inverse problem in signal processing and the prototypical model for recovering a signal from its noisy measurement.
On Learned Operator Correction in Inverse Problems
We discuss the possibility to learn a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to obtain regularised reconstructions.
Rapid Whole-Heart CMR with Single Volume Super-resolution
Conclusion: This paper demonstrates the potential of using a residual U-Net for super-resolution reconstruction of rapidly acquired low-resolution whole heart bSSFP data within a clinical setting.
Multi-Scale Learned Iterative Reconstruction
Applicability of these methods to large scale inverse problems is however limited by the available memory for training and extensive training times, the latter due to computationally expensive forward models.
Networks for Nonlinear Diffusion Problems in Imaging
By design, we obtain a nonlinear network architecture that is well suited for diffusion related problems in imaging.
Approximate k-space models and Deep Learning for fast photoacoustic reconstruction
We present a framework for accelerated iterative reconstructions using a fast and approximate forward model that is based on k-space methods for photoacoustic tomography.
Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease
In this study we investigated the effect of different radial sampling patterns on the accuracy of a CNN.
Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks
The mathematical problem for Electrical Impedance Tomography (EIT) is a highly nonlinear ill-posed inverse problem requiring carefully designed reconstruction procedures to ensure reliable image generation.
Model based learning for accelerated, limited-view 3D photoacoustic tomography
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up.
