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Yves Wiaux
Researcher at Heriot-Watt University
Publications - 216
Citations - 4530
Yves Wiaux is an academic researcher from Heriot-Watt University. The author has contributed to research in topics: Compressed sensing & Iterative reconstruction. The author has an hindex of 36, co-authored 209 publications receiving 3918 citations. Previous affiliations of Yves Wiaux include University of Geneva & Université catholique de Louvain.
Papers
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Journal ArticleDOI
Compressed sensing imaging techniques for radio interferometry
Yves Wiaux,Yves Wiaux,Laurent Jacques,Laurent Jacques,Gilles Puy,Anna M. M. Scaife,Pierre Vandergheynst +6 more
TL;DR: In this article, a generic imaging technique based on convex optimization for global minimization problems is proposed for radiometric reconstruction of sparse or compressible signals in radio interferometry.
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A Novel Sampling Theorem on the Sphere
Jason D. McEwen,Yves Wiaux +1 more
TL;DR: This work develops a novel sampling theorem on the sphere and corresponding fast algorithms by associating the sphere with the torus through a periodic extension and highlights the advantages of the sampling theorem in the context of potential applications, notably in the field of compressive sampling.
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Quantitative Comparison of Reconstruction Methods for Intra-Voxel Fiber Recovery From Diffusion MRI
Alessandro Daducci,Erick J. Canales-Rodríguez,Maxime Descoteaux,Eleftherios Garyfallidis,Yaniv Gur,Ying-Chia Lin,Merry Mani,Sylvain Merlet,Michael Paquette,Alonso Ramirez-Manzanares,Marco Reisert,Paulo Rodrigues,Farshid Sepehrband,Emmanuel Caruyer,Jeiran Choupan,Rachid Deriche,Mathews Jacob,Gloria Menegaz,V. Prckovska,Mariano Rivera,Yves Wiaux,Jean-Philippe Thiran +21 more
TL;DR: Evaluated methods encompass a mixture of classical techniques well known in the literature such as diffusion tensor, Q-Ball and diffusion spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest.
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Sparsity Averaging Reweighted Analysis (SARA): a novel algorithm for radio‐interferometric imaging
TL;DR: It is shown through simulations that the proposed approach outperforms state-of-the-art imaging methods in the field, which are based on the assumption of signal sparsity in a single basis only.
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On Variable Density Compressive Sampling
TL;DR: A coherence-driven optimization procedure for variable density sampling by use of convex optimization algorithms is advocated and a refinement of the technique when prior information is available on the signal support in the sparsity basis is proposed.