Near optimal compressed sensing without priors: Parametric SURE Approximate Message Passing

TL;DR

This paper introduces a novel parametric SURE-AMP algorithm that adaptively optimizes denoisers within the AMP framework, achieving near Bayesian optimal recovery without prior knowledge and significantly improving speed over existing methods.

Contribution

The paper proposes a new SURE-based parametric denoiser integrated with AMP, enabling adaptive, prior-free, near-optimal compressed sensing recovery with faster performance.

Findings

Achieves state-of-the-art recovery accuracy.

Runs more than 20 times faster than EM-GM-GAMP.

Demonstrates effectiveness on Bernoulli-Gaussian, k-dense, and Student's-t signals.

Abstract

Both theoretical analysis and empirical evidence confirm that the approximate message passing (AMP) algorithm can be interpreted as recursively solving a signal denoising problem: at each AMP iteration, one observes a Gaussian noise perturbed original signal. Retrieving the signal amounts to a successive noise cancellation until the noise variance decreases to a satisfactory level. In this paper we incorporate the Stein's unbiased risk estimate (SURE) based parametric denoiser with the AMP framework and propose the novel parametric SURE-AMP algorithm. At each parametric SURE-AMP iteration, the denoiser is adaptively optimized within the parametric class by minimizing SURE, which depends purely on the noisy observation. In this manner, the parametric SURE-AMP is guaranteed with the best-in-class recovery and convergence rate. If the parameter family includes the families of the mimimum mean squared error (MMSE) estimators, we are able to achieve the Bayesian optimal AMP performance without knowing the signal prior. In the paper, we resort to the linear parameterization of the SURE based denoiser and propose three different kernel families as the base functions. Numerical simulations with the Bernoulli-Gaussian, $k$-dense and Student's-t signals demonstrate that the parametric SURE-AMP does not only achieve the state-of-the-art recovery but also runs more than 20 times faster than the EM-GM-GAMP algorithm.