Permutation Enhanced Parallel Reconstruction with A Linear Compressive Sampling Device

TL;DR

This paper introduces a permutation enhanced parallel reconstruction method for compressive sampling that reduces computational complexity and measurement requirements by dividing the signal into segments and applying permutation, suitable for big data processing.

Contribution

It proposes a novel permutation enhanced parallel reconstruction architecture using a linear compressive sampling device, improving efficiency and measurement reduction.

Findings

Parallel reconstruction reduces computational complexity.

Permutation decreases the number of measurements needed.

Method is suitable for large-scale data processing.

Abstract

In this letter, a permutation enhanced parallel reconstruction architecture for compressive sampling is proposed. In this architecture, a measurement matrix is constructed from a block-diagonal sensing matrix and the sparsifying basis of the target signal. In this way, the projection of the signal onto the sparsifying basis can be divided into several segments and all segments can be reconstructed in parallel. Thus, the computational complexity and the time for reconstruction can be reduced significantly. This feature is especially appealing for big data processing. Furthermore, to reduce the number of measurements needed to achieve the desired reconstruction error performance, permutation is introduced for the projection of the signal. It is shown that the permutation can be performed implicitly by using a pre-designed measurement matrix. Thus, the permutation enhanced parallel reconstruction can be achieved with a linear compressive sampling device.