invited seminar

Interferometric Lensless Endoscopy: Rank-one Projections of Image Frequencies with Speckle Illuminations

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.

Keep the phase! Signal recovery in phase-only compressive sensing

(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.

Time for dithering! Quantized random embeddings with RIP random matrices

(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.

A simple gradient descent algorithm for blind gain calibration of randomized sensing devices

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

Consistent Basis Pursuit for Low-Complexity Signal Estimates in Quantized Compressed Sensing

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

Quantized compressed sensing and quasi-isometric embeddings

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.

Compressed sensing, quantization and quasi-isometric embedding

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.

What can we learn from the Compressed Sensing theory?

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.