Information Theoretic Approach to L-Estimators

Alex Dytso, Martina Cardone, Cynthia Rush

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publication55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781665458283
StatePublished - 2021
Event55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 - Virtual, Pacific Grove, United States
Duration: Oct 31 2021Nov 3 2021

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Conference55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
Country/TerritoryUnited States
CityVirtual, Pacific Grove

Bibliographical note

Funding Information:
The work of M. Cardone was supported in part by the U.S. National Science Foundation under Grant CCF-1849757.

Publisher Copyright:
© 2021 IEEE.


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