Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition
of sparse or compressible signals that can be well approximated by just K¿ N elements from …

Sparse coding consists in representing signals as sparse linear combinations of atoms
selected from a dictionary. We consider an extension of this framework where the atoms are …

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to
lower energy consumption in wireless body area networks for electrocardiogram (ECG) …

Developed in this book are several deep connections between time-frequency
(Fourier/Gabor) analysis and time-scale (wavelet) analysis, emphasizing the powerful …

Sparsity has been widely utilized in magnetic resonance imaging (MRI) to reduce k-space
sampling. According to structured sparsity theories, fewer measurements are required for …

Compressed sensing (CS) has been applied to magnetic resonance imaging for the
acceleration of data collection. However, existing CS techniques usually produce images …

We study several extensions of Besov spaces; these extensions include the oscillation
spaces O ps, s' which take into account correlations between the positions of large wavelet …

In this paper, we study the Besov regularity of Lévy white noises on the d-dimensional torus.
Due to their rough sample paths, the white noises that we consider are defined as …

Purpose To present and validate a new method that formalizes a direct link between k‐
space and wavelet domains to apply separate undersampling and reconstruction for high …

Frame theory is nowadays a fundamental research area in mathematics, computer science,
and engineering with many exciting applications in a variety of different fields. Introduced in …