A5041620227- Sparse and Compressive Sensing Techniques
- Radio Astronomy Observations and Technology
- Advanced MRI Techniques and Applications
- Image and Signal Denoising Methods
- Photoacoustic and Ultrasonic Imaging
- Medical Imaging Techniques and Applications
- Soil Moisture and Remote Sensing
- Cosmology and Gravitation Theories
- Microwave Imaging and Scattering Analysis
- Geophysics and Gravity Measurements
- Advanced Neuroimaging Techniques and Applications
- Ultrasound Imaging and Elastography
- Optical measurement and interference techniques
- Antenna Design and Optimization
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Seismic Imaging and Inversion Techniques
- Advanced SAR Imaging Techniques
- Ultrasonics and Acoustic Wave Propagation
- MRI in cancer diagnosis
- Atomic and Subatomic Physics Research
- Numerical methods in inverse problems
- Mathematical Analysis and Transform Methods
- Astrophysics and Cosmic Phenomena
- Advanced Image Processing Techniques
- NMR spectroscopy and applications
Sensors (United States)
2011-2025
Heriot-Watt University
2015-2024
Heriot-Watt University Malaysia
2013-2022
École Polytechnique Fédérale de Lausanne
2006-2016
University of Geneva
2010-2014
University of Lausanne
2013-2014
Centre d'Imagerie BioMedicale
2014
École Normale Supérieure - PSL
2013
Charles Humbert 8
2012
UCLouvain
2002-2010
The Low Frequency Array (LOFAR) is an ideal instrument to conduct deep extragalactic surveys. It has a large field of view and sensitive large-scale compact emission. is, however, very challenging synthesize thermal noise limited maps at full resolution, mainly because the complexity low-frequency sky direction dependent effects (phased array beams ionosphere). In this first paper series, we present new calibration imaging pipeline that aims producing high fidelity, dynamic range images with...
Radio interferometry probes astrophysical signals through incomplete and noisy Fourier measurements. The theory of compressed sensing demonstrates that such measurements may actually suffice for accurate reconstruction sparse or compressible signals. We propose new generic imaging techniques based on convex optimization global minimization problems defined in this context. versatility the framework notably allows introduction specific prior information signals, which offers possibility...
Validation is arguably the bottleneck in diffusion magnetic resonance imaging (MRI) community. This paper evaluates and compares 20 algorithms for recovering local intra-voxel fiber structure from MRI data based on results of "HARDI reconstruction challenge" organized context "ISBI 2012" conference. Evaluated methods encompass a mixture classical techniques well known literature such as tensor, Q-Ball spectrum imaging, inspired by recent theory compressed sensing also brand new approaches...
We develop a novel sampling theorem on the sphere and corresponding fast algorithms by associating with torus through periodic extension. The fundamental property of any is number samples required to represent band-limited signal. To exactly signal at L, all theorems require O(L^2) samples. However, our requires less than half other equiangular an asymptotically identical, but smaller, Gauss-Legendre theorem. complexity scale as O(L^3), however, continual use Fourier transforms reduces...
Inspired by the recently proposed magnetic resonance fingerprinting (MRF) technique, we develop a principled compressed sensing framework for quantitative MRI. The three key components are random pulse excitation sequence following MRF EPI subsampling strategy, and an iterative projection algorithm that imposes consistency with Bloch equations. We show that, theoretically, as long possesses appropriate form of persistent excitation, able to accurately recover proton density, T1, T2,...
In a recent article series, the authors have promoted convex optimization algorithms for radio-interferometric imaging in framework of compressed sensing, which leverages sparsity regularization priors associated inverse problem and defines minimization image reconstruction. This approach was shown, theory through simulations simple discrete visibility setting, to potential outperform significantly CLEAN its evolutions. this work, we leverage versatility solving problems both handle...
We propose a novel algorithm for image reconstruction in radio interferometry. The ill-posed inverse problem associated with the incomplete Fourier sampling identified by visibility measurements is regularized assumption of average signal sparsity over representations multiple wavelet bases. algorithm, defined versatile framework convex optimization, dubbed Sparsity Averaging Reweighted Analysis (SARA). show through simulations that proposed approach outperforms state-of-the-art imaging...
We introduce a new paradigm for solving regularized variational problems. These are typically formulated to address ill-posed inverse problems encountered in signal and image processing. The objective function is traditionally defined by adding regularization data fit term, which subsequently minimized using iterative optimization algorithms. Recently, several works have proposed replace the operator related more sophisticated denoiser. approaches, known as plug-and-play (PnP) methods, shown...
Abstract Radio-interferometric imaging entails solving high-resolution high-dynamic-range inverse problems from large data volumes. Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for precision, well beyond CLEAN’s capability. These range advanced proximal algorithms propelled by handcrafted regularization operators, such as the SARA family, to hybrid plug-and-play (PnP) learned denoisers, AIRI. Optimization and PnP structures...
A new formalism is derived for the analysis and exact reconstruction of band-limited signals on sphere with directional wavelets. It represents an evolution wavelet developed by Antoine & Vandergheynst (1999) Wiaux et al. (2005). The translations wavelets at any point their proper rotations are still defined through continuous three-dimensional rotations. dilations directly in harmonic space a kernel dilation, which modification existing dilation. family factorized steerable functions...
We advocate an optimization procedure for variable density sampling in the context of compressed sensing. In this perspective, we introduce a minimization problem coherence between sparsity and sensing bases, whose solution provides optimized profile. This is solved with use convex algorithms. also propose refinement our technique when prior information available on signal support basis. The effectiveness method confirmed by numerical experiments. Our results provide theoretical underpinning...
We propose a novel compressed sensing technique to accelerate the magnetic resonance imaging (MRI) acquisition process. The method, coined spread spectrum MRI or simply s2MRI, consists of pre-modulating signal interest by linear chirp before random k-space under-sampling, and then reconstructing with non-linear algorithms that promote sparsity. effectiveness procedure is theoretically underpinned optimization coherence between sparsity bases. proposed thoroughly studied means numerical...
We discuss a novel sparsity prior for compressive imaging in the context of theory compressed sensing with coherent redundant dictionaries, based on observation that natural images exhibit strong average over multiple frames. test our and associated algorithm, an analysis reweighted $\ell_1$ formulation, through extensive numerical simulations spread spectrum random Gaussian acquisition schemes. Our results show outperforms state-of-the-art priors promote single orthonormal basis or frame,...
In the context of next generation radio telescopes, like Square Kilometre Array, efficient processing large-scale datasets is extremely important. Convex optimisation tasks under compressive sensing framework have recently emerged and provide both enhanced image reconstruction quality scalability to increasingly larger data sets. We focus herein mainly on propose two new convex algorithmic structures able solve arising in radio-interferometric imaging. They rely proximal splitting...
We propose a Bayesian uncertainty quantification method for large-scale imaging inverse problems. Our applies to all models that are log-concave, where maximum posteriori (MAP) estimation is convex optimization problem. The framework analyze the confidence in specific structures observed MAP estimates (e.g., lesions medical imaging, celestial sources astronomical imaging), enable using them as evidence inform decisions and conclusions. Precisely, following decision theory, we seek assert...
Wavelets on the sphere are reintroduced and further developed independently of original group theoretic formalism, in an equivalent, but more straightforward approach. These developments motivated by interest scale-space analysis cosmic microwave background (CMB) anisotropies sky. A new, self-consistent, practical approach to wavelet filtering is developed. It also established that inverse stereographic projection a plane (i.e., Euclidean wavelet) leads spherical wavelet). This new...
We describe S2LET, a fast and robust implementation of the scale-discretised wavelet transform on sphere. Wavelets are constructed through tiling harmonic line can be used to probe spatially localised, scale-depended features signals The was developed previously reduces needlet in axisymmetric case. reconstruction signal from its wavelets coefficients is made exact here use sampling theorem Moreover, multiresolution algorithm presented capture all information each scale minimal number...
Next-generation radio interferometers, such as the Square Kilometre Array (SKA), will revolutionise our understanding of universe through their unprecedented sensitivity and resolution. However, to realise these goals significant challenges in image data processing need be overcome. The standard methods interferometry for reconstructing images, CLEAN, have served community well over last few decades survived largely because they are pragmatic. produce reconstructed inter\-ferometric images...
We introduce a new class of iterative image reconstruction algorithms for radio interferometry, at the interface convex optimization and deep learning, inspired by plug-and-play methods. The approach consists in learning prior model training neural network (DNN) as denoiser, substituting it handcrafted proximal regularization operator an algorithm. proposed AIRI (``AI Regularization radio-interferometric Imaging'') framework, imaging complex intensity structure with diffuse faint emission...
Abstract Plug-and-Play (PnP) algorithms are appealing alternatives to proximal when solving inverse imaging problems. By learning a Deep Neural Network (DNN) denoiser behaving as operator, one waives the computational complexity of optimisation induced by sophisticated image priors, and sub-optimality handcrafted priors compared DNNs. Such features highly desirable in radio-interferometric (RI) imaging, where precision scalability reconstruction process key. In previous work, we introduced...
We question the global universe isotropy by probing alignment of local structures in cosmic microwave background (CMB) radiation. The original method proposed relies on a steerable wavelet decomposition CMB signal sphere. analysis first-year Wilkinson Microwave Anisotropy Probe data identifies mean preferred plane with normal direction close to dipole axis, and this plane, very ecliptic poles axis. Previous statistical anisotropy results are thereby synthesized, but further analyses still...
For the next generation of radio interferometric telescopes it is paramount importance to incorporate wide field-of-view (WFOV) considerations in imaging, otherwise fidelity reconstructed images will suffer greatly. We extend compressed sensing techniques for imaging a WFOV and recover spherical coordinate space which they naturally live, eliminating any distorting projection. The effectiveness spread spectrum phenomenon, highlighted recently by one authors, enhanced when going WFOV, while...
We consider the probe of astrophysical signals through radio interferometers with a small field view and baselines non-negligible constant component in pointing direction. In this context, visibilities measured essentially identify noisy incomplete Fourier coverage product planar linear chirp modulation. light recent theory compressed sensing perspective defining best possible imaging techniques for sparse signals, we analyse related spread spectrum phenomenon suggest its universality...