Wavelets have been used extensively for several years now in astronomy for many purposes, ranging from data filtering and deconvolution to star and galaxy detection or cosmic-ray removal. More recent sparse representations such as ridgelets or curvelets have also been proposed for the detection of anisotropic features such as cosmic strings in the cosmic microwave background. We review in this paper a range of methods based on sparsity that have been proposed for astronomical data analysis. We also discuss the impact of compressed sensing, the new sampling theory, in astronomy for collecting the data, transferring them to earth or reconstructing an image from incomplete measurements
We develop techniques to solve ill-posed inverse problems on the sphere by sparse regularization, ex...
The wavelet representation of a signal offers greater flexibility in de-noising astronomical spectra...
This book presents the state of the art in sparse and multiscale image and signal processing, coveri...
Recent advances in signal processing have focused on the use of sparse representations in various ap...
Radio interferometry probes astrophysical signals through incomplete and noisy Fourier measurements....
We propose an algorithm for the reconstruction of the signal induced by cosmic strings in the cosmic...
International audienceSparse decompositions were mainly developed to optimize the signal or the imag...
This poster is a summary of recent work published in: Spread spectrum for imaging techniques in radi...
We outline digital implementations of two newly developed multiscale representation systems, namely,...
Radio interferometry probes astrophysical signals through incomplete and noisy Fourier measurements....
We propose a novel algorithm for image reconstruction in radio interferometry. The ill-posed inverse...
Compressive sampling is a new paradigm for sampling, based on sparseness of signals or sig...
Compressive sampling is a new paradigm for sampling, based on sparseness of signals or signal repres...
Abstract—Recent developments in [1] and [2] introduced a novel regularization method for compressive...
Recent developments in Carrillo et al. (2012) and Carrillo et al. (2013) introduced a novel regulari...
We develop techniques to solve ill-posed inverse problems on the sphere by sparse regularization, ex...
The wavelet representation of a signal offers greater flexibility in de-noising astronomical spectra...
This book presents the state of the art in sparse and multiscale image and signal processing, coveri...
Recent advances in signal processing have focused on the use of sparse representations in various ap...
Radio interferometry probes astrophysical signals through incomplete and noisy Fourier measurements....
We propose an algorithm for the reconstruction of the signal induced by cosmic strings in the cosmic...
International audienceSparse decompositions were mainly developed to optimize the signal or the imag...
This poster is a summary of recent work published in: Spread spectrum for imaging techniques in radi...
We outline digital implementations of two newly developed multiscale representation systems, namely,...
Radio interferometry probes astrophysical signals through incomplete and noisy Fourier measurements....
We propose a novel algorithm for image reconstruction in radio interferometry. The ill-posed inverse...
Compressive sampling is a new paradigm for sampling, based on sparseness of signals or sig...
Compressive sampling is a new paradigm for sampling, based on sparseness of signals or signal repres...
Abstract—Recent developments in [1] and [2] introduced a novel regularization method for compressive...
Recent developments in Carrillo et al. (2012) and Carrillo et al. (2013) introduced a novel regulari...
We develop techniques to solve ill-posed inverse problems on the sphere by sparse regularization, ex...
The wavelet representation of a signal offers greater flexibility in de-noising astronomical spectra...
This book presents the state of the art in sparse and multiscale image and signal processing, coveri...