De-noising procedures for frame operators

TL;DR

This paper investigates de-noising techniques for frames, especially tight frames, in signal processing, highlighting how leveraging frame structure and correlations enhances noise reduction effectiveness.

Contribution

It introduces practical de-noising methods that utilize frame-induced correlations, bridging mathematical theory and engineering practice, with validation through simulations.

Findings

Frame structure improves de-noising accuracy.

Correlation-aware methods outperform traditional techniques.

Simulations confirm the benefits of using frame-induced correlations.

Abstract

The present paper provides a comprehensive study of de-noising properties of frames and, in particular, tight frames, which constitute one of the most popular tools in contemporary signal processing. The objective of the paper is to bridge the existing gap between mathematical and statistical theories on one hand and engineering practice on the other and explore how one can take advantage of a specific structure of a frame in contrast to an arbitrary collection of vectors or an orthonormal basis. For both the general and the tight frames, the paper presents a set of practically implementable de-noising techniques which take frame induced correlation structures into account. These results are supplemented by an examination of the case when the frame is constructed as a collection of orthonormal bases. In particular, recommendations are given for aggregation of the estimators at the stage of frame coefficients. The paper is concluded by a finite sample simulation study which confirms that taking frame structure and frame induced correlations into account indeed improves de-noising precision.