"Self-Wiener" Filtering: Data-Driven Deconvolution of Deterministic Signals

TL;DR

This paper introduces a novel, non-iterative, data-driven frequency-domain deconvolution method for deterministic signals, which adapts thresholds based on noise levels to improve recovery accuracy across different SNR regimes.

Contribution

It proposes a new threshold-based estimator that mimics an ideal Wiener filter, with theoretical analysis and practical advantages for bandlimited and sparse signals.

Findings

Enhanced noise suppression at low SNR

Approaches optimal solution at high SNR

Significant MSE improvements over existing methods

Abstract

We consider the problem of robust deconvolution, and particularly the recovery of an unknown deterministic signal convolved with a known filter and corrupted by additive noise. We present a novel, non-iterative data-driven approach. Specifically, our algorithm works in the frequency-domain, where it tries to mimic the optimal unrealizable non-linear Wiener-like filter as if the unknown deterministic signal were known. This leads to a threshold-type regularized estimator, where the threshold at each frequency is determined in a data-driven manner. We perform a theoretical analysis of our proposed estimator, and derive approximate formulas for its Mean Squared Error (MSE) at both low and high Signal-to-Noise Ratio (SNR) regimes. We show that in the low SNR regime our method provides enhanced noise suppression, and in the high SNR regime it approaches the optimal unrealizable solution. Further, as we demonstrate in simulations, our solution is highly suitable for (approximately) bandlimited or frequency-domain sparse signals, and provides a significant gain of several dBs relative to other methods in the resulting MSE.