Structure-Constrained Basis Pursuit for Compressed Sensing

TL;DR

This paper introduces Structure-Constrained Basis Pursuit (SCBP), a novel compressed sensing recovery method that leverages known signal structures to improve accuracy and enable higher compression ratios.

Contribution

The paper proposes SCBP, a new approach that incorporates known signal structures into basis pursuit, enhancing recovery accuracy without additional sampling.

Findings

23.8% lower average error at 5:1 compression ratio compared to vanilla BP

Applicable to various structured data like speech and waveforms

Enables higher compression ratios with similar error levels

Abstract

In compressive sensing (CS) theory, as the number of samples is decreased below a minimum threshold, the average error of the recovery increases. Sufficient sampling is either required for quality reconstruction or the error is resignedly accepted. However, most CS work has not taken advantage of the inherent structure in a variety of signals relevant to engineering applications. Hence, this paper proposes a new method of recovery built on basis pursuit (BP), called Structure-Constrained Basis Pursuit (SCBP), that constrains signals based on known structure rather than through extra sampling. Preliminary assessments of this method on TIMIT recordings of the speech phoneme /aa/ show a substantial decrease in error: with a fixed 5:1 compression ratio the average recovery error is 23.8% lower versus vanilla BP. More significantly, this method can be applied to any CS application that samples structured data, such as FSK waveforms, speech, and tones. In these cases, higher compression ratios can be reached with comparable error.