Work

Robust One-bit Compressed Sensing With Manifold Data

Year: 2019

Type: conference-paper

Abstract: We study one-bit compressed sensing for signals on a low-dimensional manifold. We introduce two computationally efficient reconstruction algorithms that only require access to a geometric multi-resolution analysis approximation of the manifold. We derive rigorous reconstruction guarantees for these methods in the scenario that the measurements are subgaussian and show that they are robust with respect to both pre- and post-quantization noise. Our results substantially improve upon earlier work in this direction.

Source: International Conference on Sampling Theory and Applications

DOI: 10.1109/sampta45681.2019.9030809

Authors: Sjoerd Dirksen, Mark Iwen, Sara Krause-Solberg, Johannes Maly

Institutions: Utrecht University, Michigan State University, Technical University of Munich, RWTH Aachen University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3011377254

FWCI: 0.87

Cites: 6

Cited by: 3

Related to: 10