Abstract: Lensless endoscopy (LE) with multicore fibers (MCF) enables fluorescent imaging of biological samples at cellular scale. In this talk, we will see that under a common far-field approximation, the corresponding imaging process is tantamount to collecting multiple rank-one projections (ROP) of an Hermitian “interferometric” matrix–a matrix encoding a subsampling of the Fourier transform of the sample image.

(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.

(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.

Invited by Simon Foucart.

In the context of my participation to the PhD “soutenance” of Marwa Chaffi (CentraleSupélec, Rennes, France).

In the context of my participation to the PhD “pré-soutenance” of Marwa Chaffi (CentraleSupélec, Rennes, France).

Abstract: The advent of Compressed Sensing (CS) ten years ago has precipitated a radical re-thinking of signal acquisition, sensing, processing and transmission system design. A significant aspect of such systems is quantization (or digitization) of the acquired data before further processing or for the purpose of transmission and compression.

Abstract: The advent of Compressed Sensing (CS) ten years ago has precipitated a radical re-thinking of signal acquisition, sensing, processing and transmission system design. A significant aspect of such systems is quantization (or digitization) of the acquired data before further processing or for the purpose of transmission and compression.

Abstract: The recent theory of Compressed Sensing (CS) induces a revolution in the design of signal sensors and of imaging devices. By the advent of increased computing capabilities, along with recent theoretical and numerical breakthroughs in the fields of Image Processing, Sparse Signal Representations, Inverse Problem solving and Convex Optimization, the term Sensing is no more a synonym for readily rendering human readable signals.