POCS-based framework of signal reconstruction from generalized non-uniform samples

TL;DR

This paper develops a comprehensive POCS-based framework for reconstructing signals from highly general non-uniform samples across various Hilbert spaces, ensuring convergence and robustness even with heterogeneous or insufficient data.

Contribution

It formalizes POCS for the most general non-uniform sampling scenarios, introduces a parallelized version, and provides a multiplierless implementation that accelerates convergence.

Findings

Proves unconditional convergence of POCS iterates for general non-uniform samples.

Introduces a parallelized POCS algorithm with connections to pseudo-inversion.

Provides a multiplierless implementation that speeds up convergence.

Abstract

We formalize the use of projections onto convex sets (POCS) for the reconstruction of signals from non-uniform samples in their highest generality. This covers signals in any Hilbert space $\mathscr H$, including multi-dimensional and multi-channel signals, and samples that are most generally inner products of the signals with given kernel functions in $\mathscr H$. An attractive feature of the POCS method is the unconditional convergence of its iterates to an estimate that is consistent with the samples of the input, even when these samples are of very heterogeneous nature on top of their non-uniformity, and/or under insufficient sampling. Moreover, the error of the iterates is systematically monotonically decreasing, and their limit retrieves the input signal whenever the samples are uniquely characteristic of this signal. In the second part of the paper, we focus on the case where the sampling kernel functions are orthogonal in $\mathscr H$, while the input may be confined in a smaller closed space $\mathscr A$ (of bandlimitation for example). This covers the increasingly popular application of time encoding by integration, including multi-channel encoding. We push the analysis of the POCS method in this case by giving a special parallelized version of it, showing its connection with the pseudo-inversion of the linear operator defined by the samples, and giving a multiplierless discrete-time implementation of it that paradoxically accelerates the convergence of the iteration.