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Found 18 papers, 4 papers with code
R2D2 image reconstruction with model uncertainty quantification in radio astronomy
The ``Residual-to-Residual DNN series for high-Dynamic range imaging'' (R2D2) approach was recently introduced for Radio-Interferometric (RI) imaging in astronomy.
Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2
We propose a new approach for non-Cartesian magnetic resonance image reconstruction.
The R2D2 deep neural network series paradigm for fast precision imaging in radio astronomy
Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN's capability.
Plug-and-play imaging with model uncertainty quantification in radio astronomy
In a previous work, we introduced a class of convergent PnP algorithms, dubbed AIRI, relying on a forward-backward algorithm, with a differentiable data-fidelity term and dynamic range-specific denoisers trained on highly pre-processed unrelated optical astronomy images.
CLEANing Cygnus A deep and fast with R2D2
A novel deep learning paradigm for synthesis imaging by radio interferometry in astronomy was recently proposed, dubbed "Residual-to-Residual DNN series for high-Dynamic range imaging" (R2D2).
Deep network series for large-scale high-dynamic range imaging
We propose a residual DNN series approach, also interpretable as a learned version of matching pursuit, where the reconstructed image is a sum of residual images progressively increasing the dynamic range, and estimated iteratively by DNNs taking the back-projected data residual of the previous iteration as input.
Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers
The approach consists in learning a prior image model by training a deep neural network (DNN) as a denoiser, and substituting it for the handcrafted proximal regularization operator of an optimization algorithm.
Learning Maximally Monotone Operators for Image Recovery
Recently, several works have proposed to replace the operator related to the regularization by a more sophisticated denoiser.
Automated Theorem Proving
Image Restoration
Optimization and Control
Image and Video Processing
47H05, 90C25, 90C59, 65K10, 49M27, 68T07, 68U10, 94A08
CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction
Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy computations of a matched-filtering step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications.
CoverBLIP: scalable iterative matched filtering for MR Fingerprint recovery
Current proposed solutions for the high dimensionality of the MRF reconstruction problem rely on a linear compression step to reduce the matching computations and boost the efficiency of fast but non-scalable searching schemes such as the KD-trees.
Wideband Super-resolution Imaging in Radio Interferometry via Low Rankness and Joint Average Sparsity Models (HyperSARA)
We propose a new approach within the versatile framework of convex optimization to solve the radio-interferometric wideband imaging problem.
Image and Video Processing Instrumentation and Methods for Astrophysics Signal Processing
Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling and Anatomical Priors
High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex neuronal fiber configurations, albeit, at the cost of long acquisition times.
Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery
We adopt data structure in the form of cover trees and iteratively apply approximate nearest neighbour (ANN) searches for fast compressed sensing reconstruction of signals living on discrete smooth manifolds.
Robust sparse image reconstruction of radio interferometric observations with purify
However, they produce reconstructed inter\-ferometric images that are limited in quality and scalability for big data.
Instrumentation and Methods for Astrophysics
Directional spin wavelets on the sphere
We construct a directional spin wavelet framework on the sphere by generalising the scalar scale-discretised wavelet transform to signals of arbitrary spin.
Information Theory Instrumentation and Methods for Astrophysics Information Theory
Sparsity averaging for radio-interferometric imaging
We propose a novel regularization method for compressive imaging in the context of the compressed sensing (CS) theory with coherent and redundant dictionaries.
A Compressed Sensing Framework for Magnetic Resonance Fingerprinting
Inspired by the recently proposed Magnetic Resonance Fingerprinting (MRF) technique, we develop a principled compressed sensing framework for quantitative MRI.
Information Theory Information Theory
PURIFY: a new approach to radio-interferometric imaging
This approach was shown, in theory and through simulations in a simple discrete visibility setting, to have the potential to outperform significantly CLEAN and its evolutions.
Instrumentation and Methods for Astrophysics
