compressive sensing

Abstract: Fourier Transform Interferometry (FTI) is an appealing Hyperspectral (HS) imaging modality for many applications demanding high spectral resolution, e.g., in fluorescence microscopy. However, the effective resolution of FTI is limited by the durability (or photobleaching) of biological elements when exposed to illuminating light.

(invited by H. Tyagi and M. Cucuringu) Abstract: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals (e.g., sparse vectors in a given basis, low-rank matrices) with quantized, finite precision representations, i.

Joint work with C. Xu and V. Schellekens.

Invited by Simon Foucart.

Abstract: This work presents a compact image sensor architecture with end-of-column digital processing dedicated to perform embedded object recognition. The architecture takes advantage of a Compressed Sensing (CS) scheme to extract compressed features and to reduce data dimensionality based on a low footprint pseudo random data mixing.

Abstract: In this paper, we further expand on the work in [1] that focused on the localization of targets in a 2D space using 1-bit dithered measurements coming from a 2 receiving antennae radar.

Abstract: This paper introduces a single-pixel HyperSpectral (HS) imaging framework based on Fourier Transform Interferometry (FTI). By combining a space-time coding of the light illumination with partial interferometric observations of a collimated light beam (observed by a single pixel), our system benefits from (i) reduced measurement rate and light-exposure of the observed object compared to common (Nyquist) FTI imagers, and (ii) high spectral resolution as desirable in, eg, Fluorescence Spectroscopy (FS).