compressive sensing

Interferometric single-pixel imaging with a multicore fiber

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.

The importance of phase in complex compressive sensing

(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.

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.

Compressive Imaging Through Optical Fiber with Partial Speckle Scanning

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.

Going Below and Beyond, Off-the-Grid Velocity Estimation from 1-bit Radar Measurements

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.

The Importance of Phase in Complex Compressive Sensing

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.

Close Encounters of the Binary Kind: Signal Reconstruction Guarantees for Compressive Hadamard Sampling with Haar Wavelet Basis

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.

A Variable Density Sampling Scheme for Compressive Fourier Transform Interferometry

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 of biological elements when exposed to illuminating light.

An Analog-to-Information VGA Image Sensor Architecture for Support Vector Machine on Compressive Measurements

Abstract: This work presents a compact VGA (480 × 640) CMOS Image Sensor (CIS) architecture with dedicated end-of-column Compressive Sensing (CS) scheme allowing embedded object recognition. The architecture takes advantage of a low-footprint pseudo-random data mixing circuit and a first order incremental Sigma-Delta (ΣΔ) Analog to Digital Converter (ADC) to extract compressed features.

Compressive Single-pixel Fourier Transform Imaging using Structured Illumination

Abstract: Single Pixel (SP) imaging is now a reality in many applications, eg, biomedical ultrathin endoscope and fluorescent spectroscopy. In this context, many schemes exist to improve the light throughput of these device, eg, using structured illumination driven by compressive sensing theory.