compressive sensing

Updates: (22/04/26) Great news!! A fast, butterfly (aka FFT-like), implementation of the Noiselet Transform (see below) is now integrated into the LazyLinop toolbox – “a python toolbox to ease and accelerate computations with (“matrix-free”) linear operators.

Abstract: In this paper, we tackle the issue of implementing a dithering procedure for the 1-bit quantization of radar signals that is able to generate high-quality estimates while remaining a low-complexity and cost-efficient solution.

Abstract: Dithering techniques, through the addition of a (random) signal before the Analog to Digital Converter (ADC), are widely used in acquisition chain designs for their ability to shape the quantization noise, e.

Abstract: Lensless illumination single-pixel imaging with a multicore fiber (MCF) is a computational imaging technique that enables potential endoscopic observations of biological samples at cellular scale. In this work, we show that this technique is tantamount to collecting multiple symmetric rank-one projections (SROP) of a Hermitian interferometric matrix – a matrix encoding the spectral content of the sample image.

(joint work with T. Feuillen) Abstract: In this talk, we consider the estimation of a sparse (or low-complexity) signal from the phase of complex random measurements, a “phase-only compressive sensing” (PO-CS) scenario.

(Invited by T. Fromentèze. Joint work with Thomas Feuillen.) Abstract: In this seminar, we show how a sparse signal can be estimated from the phase of complex random measurements, in a “phase-only compressive sensing” (PO-CS) scenario.

Abstract: The lensless endoscope (LE) is a promising device to acquire in vivo images at a cellular scale. The tiny size of the probe enables a deep exploration of the tissues.

Abstract: In this paper we propose to bridge the gap between using extremely low resolution 1-bit measurements and estimating targets’ parameters, such as their velocities, that exist in a continuum, i.

Abstract: We consider the question of estimating a real low-complexity signal (such as a sparse vector or a low-rank matrix) from the phase of complex random measurements. We show that in this phase-only compressive sensing (PO-CS) scenario, we can perfectly recover such a signal with high probability and up to global unknown amplitude if the sensing matrix is a complex Gaussian random matrix and the number of measurements is large compared to the complexity level of the signal space.

Abstract: We investigate the problems of 1-D and 2-D signal recovery from subsampled Hadamard measurements using Haar wavelet as a sparsity inducing prior. These problems are of interest in, e.g., computational imaging applications relying on optical multiplexing or single-pixel imaging.