GSW: Generalized "Self-Wiener" Denoising

TL;DR

The paper introduces GSW, a flexible denoising estimator that adapts to different SNR levels, combining nonlinear shrinkage with a tunable threshold for improved performance.

Contribution

It generalizes the self-Wiener filtering method by adding a tunable parameter, enabling better adaptation to various noise conditions and analytical tractability.

Findings

GSW closely approximates the oracle MMSE at high SNR.

It maintains robustness at low SNR.

Simulation results validate its superior denoising performance.

Abstract

We revisit the recently proposed ``self-Wiener" (SW) filtering method for robust deconvolution, and generalize it to the classical denoising problem. The resulting estimator, termed generalized SW (GSW) filtering, retains the nonlinear shrinkage structure of SW but introduces a tunable threshold parameter. This tunability enables GSW to flexibly adapt to varying signal-to-noise ratio (SNR) regimes by balancing noise suppression and signal preservation. We derive closed-form expressions for its mean-square error (MSE) performance in both low- and high-SNR regimes, and demonstrate that GSW closely approximates the oracle MMSE at high SNR while maintaining strong robustness at low SNR. Simulation results validate the analytical findings, showing that GSW consistently achieves favorable denoising performance across a wide range of SNRs. Its analytical tractability, parameter flexibility, and close connection to the optimal Wiener filter structure make it a promising tool for practical applications including compressive sensing, sparse signal recovery, and domain-specific shrinkage in wavelet, Fourier, and potentially learned orthonormal representations.