Information Theoretic Approach to L-Estimators

Document Type

Conference Proceeding

Publication Date

1-1-2021

Abstract

We propose a novel way of choosing the coefficients of a class of robust estimators, known as L-estimators. Towards this end, we leverage information theoretic measures, such as the entropy and mutual information, to rigorously characterize the amount of information contained in any subset of the complete collection of order statistics. As an application, we show how the developed framework can be used for image denoising. In particular, we demonstrate that the proposed method is competitive with off-the-shelf filters, as well as with wavelet-based denoising methods, for both discrete (e.g., salt and pepper) and continuous (e.g., mixed Gaussian) noise distributions.

Identifier

85127054413 (Scopus)

ISBN

[9781665458283]

Publication Title

Conference Record Asilomar Conference on Signals Systems and Computers

External Full Text Location

https://doi.org/10.1109/IEEECONF53345.2021.9723244

ISSN

10586393

First Page

485

Last Page

489

Volume

2021-October

Grant

CCF-1849757

Fund Ref

National Science Foundation

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