The compressive sensing (CS) framework aims to ease the burden on analog-to-digital
converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse …

Radio interferometry probes astrophysical signals through incomplete and noisy Fourier
measurements. The theory of compressed sensing demonstrates that such measurements may …

In this paper, we study the problem of recovering sparse or compressible signals from uniformly
quantized measurements. We present a new class of convex optimization programs, or …

In this paper we exploit the continuous wavelet transform (CWT) on the two-dimensional
sphere S 2 , introduced previously by two of us, to build associated discrete wavelet frames. We …

The richness of natural images makes the quest for optimal representations in image processing
and computer vision challenging. The latter observation has not prevented the design …

This short note studies a variation of the compressed sensing paradigm introduced recently
by Vaswani et al., ie, the recovery of sparse signals from a certain number of linear …

Nonrigid image registration is widely used to estimate tissue deformations in highly deformable
anatomies. Among the existing methods, nonparametric registration algorithms such as …

In this work, we show that reconstructing a sparse signal from quantized compressive
measurement can be achieved in an unified formalism whatever the (scalar) quantization …

Quantization is an essential step in digitizing signals, and, therefore, an indispensable
component of any modern acquisition system. This chapter explores the interaction of …

We continue the analysis of the continuous wavelet transform on the 2-sphere, introduced in
a previous paper. After a brief review of the transform, we define and discuss the notion of …