Sensing

Compressive Fourier Transform Interferometry for Hyperspectral Imaging
In this project we propose two variants of the Fourier Transform Interferometry (FTI) imager, i.e., coded illumination-FTI (CI-FTI) and structured illumination FTI (SI-FTI), based on the theory of compressive sensing (CS). These schemes efficiently modulate light exposure temporally (in CI-FTI) or spatiotemporally (in SI-FTI). Leveraging a variable density sampling strategy recently introduced in CS, we provide near-optimal illumination strategies, so that the light exposure imposed on a biological specimen is minimized while the spectral resolution is preserved.
Mar 3, 2020
Compressive Hadamard Sensing with Haar Sparsity Basis
In this project, we compute an explicit sample-complexity bound for Hadamard-Haar systems as well as uniform and nonuniform recovery guarantees.
Mar 2, 2020
Compressive Learning
This is a collection of sub-projects gravitating around the field of Compressive (Statistical) Learning, a machine learning framework that uses inspiration from compressive sensing to relieve to computational load of learning from massive datasets.
Jan 15, 2020

Bridging 1-bit and High-Resolution Quantized Compressed Sensing with QIHT
In the framework of Quantized Compressed Sensing, we tried to bridge two extreme cases: 1-bit and high resolution quantization. The requirement of consistency of the reconstructed signal with quantized measurement led us to a new reconstruction algorithm called Quantized IHT (QIHT) that outperforms classical algorithms (IHT and BPDN) at low resolutions.
Oct 23, 2017