no code implementations • 26 Mar 2024 • Amir Aghabiglou, Chung San Chu, Arwa Dabbech, Yves Wiaux
The ``Residual-to-Residual DNN series for high-Dynamic range imaging'' (R2D2) approach was recently introduced for Radio-Interferometric (RI) imaging in astronomy.
no code implementations • 26 Mar 2024 • YiWei Chen, Chao Tang, Amir Aghabiglou, Chung San Chu, Yves Wiaux
We propose a new approach for non-Cartesian magnetic resonance image reconstruction.
no code implementations • 8 Mar 2024 • Amir Aghabiglou, Chung San Chu, Arwa Dabbech, Yves Wiaux
Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN's capability.
no code implementations • 12 Dec 2023 • Matthieu Terris, Chao Tang, Adrian Jackson, Yves Wiaux
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.
no code implementations • 6 Sep 2023 • Arwa Dabbech, Amir Aghabiglou, Chung San Chu, Yves Wiaux
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).
no code implementations • 28 Oct 2022 • Amir Aghabiglou, Matthieu Terris, Adrian Jackson, Yves Wiaux
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.
no code implementations • 25 Feb 2022 • Matthieu Terris, Arwa Dabbech, Chao Tang, Yves Wiaux
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.
2 code implementations • 24 Dec 2020 • Jean-Christophe Pesquet, Audrey Repetti, Matthieu Terris, Yves Wiaux
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
1 code implementation • 3 Oct 2018 • Mohammad Golbabaee, Zhouye Chen, Yves Wiaux, Mike Davies
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.
no code implementations • 6 Sep 2018 • Mohammad Golbabaee, Zhouye Chen, Yves Wiaux, Mike E. Davies
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.
no code implementations • 12 Jun 2018 • Abdullah Abdulaziz, Arwa Dabbech, Yves Wiaux
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
no code implementations • 8 Feb 2018 • Marica Pesce, Audrey Repetti, Anna Auría, Alessandro Daducci, Jean-Philippe Thiran, Yves Wiaux
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.
no code implementations • 23 Jun 2017 • Mohammad Golbabaee, Zhouye Chen, Yves Wiaux, Mike E. Davies
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.
1 code implementation • 7 Oct 2016 • Luke Pratley, Jason D. McEwen, Mayeul d'Avezac, Rafael E. Carrillo, Alexandru Onose, Yves Wiaux
However, they produce reconstructed inter\-ferometric images that are limited in quality and scalability for big data.
Instrumentation and Methods for Astrophysics
no code implementations • 22 Sep 2015 • Jason D. McEwen, Boris Leistedt, Martin Büttner, Hiranya V. Peiris, Yves Wiaux
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
no code implementations • 11 Feb 2014 • Rafael E. Carrillo, Jason D. McEwen, Yves Wiaux
We propose a novel regularization method for compressive imaging in the context of the compressed sensing (CS) theory with coherent and redundant dictionaries.
no code implementations • 9 Dec 2013 • Mike Davies, Gilles Puy, Pierre Vandergheynst, Yves Wiaux
Inspired by the recently proposed Magnetic Resonance Fingerprinting (MRF) technique, we develop a principled compressed sensing framework for quantitative MRI.
Information Theory Information Theory
2 code implementations • 16 Jul 2013 • Rafael E. Carrillo, Jason D. McEwen, Yves Wiaux
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