Year: 2019
Type: article
Abstract: Estimating a vector from noisy linear measurements often requires use of prior knowledge or structural constraints on for accurate reconstruction. Several recent works have considered combining linear least-squares estimation with a generic or ‘plug-in’ denoiser function that can be designed in a modular manner based on the prior knowledge about . While these methods have shown excellent performance, it has been difficult to obtain rigorous performance guarantees. This work considers plug-in denoising combined with the recently-developed vector approximate message passing (VAMP) algorithm, which is itself derived via expectation propagation techniques. It shown that the mean squared error of this ‘plug-and-play’ VAMP can be exactly predicted for high-dimensional right-rotationally invariant random and Lipschitz denoisers. The method is demonstrated on applications in image recovery and parametric bilinear estimation.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: NeTS: Medium: Massive Mobile Broadband Communications with Millimeter Wave Picocellular Networks, CAREER: Structured Nonlinear Estimation via Message Passing: Theory and Applications, G3557183362, CIF: Medium: Collaborative Research: Scalable Learning of Nonlinear Models in Large Neural Populations, Approximate Message Passing Algorithms and Networks, CIF: Small: Approximate Message Passing for Systems with Linear Mixing and Randomization, EARS: Spectrum and Infrastructure Sharing in Millimeter Wave Cellular Networks, G8876996369
OpenAlex ID: W2810458093