Work

Efficient Least Residual Greedy Algorithms for Sparse Recovery

Year: 2020

Type: article

Abstract: We present a novel stagewise strategy for improving greedy algorithms for sparse recovery. We demonstrate its efficiency both for synthesis and analysis sparse priors, where in both cases we demonstrate its computational efficiency and competitive reconstruction accuracy. In the synthesis case, we also provide theoretical guarantees for the signal recovery that are on par with the existing perfect reconstruction bounds for the relaxation based solvers and other sophisticated greedy algorithms.

Source: IEEE Transactions on Signal Processing

DOI: 10.1109/tsp.2020.2988427

Authors: Guy Leibovitz, Raja Giryes

Institution: Tel Aviv University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: European Research Council

Award: G7939141013