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.
| Original language | English (US) |
|---|---|
| Title of host publication | 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 |
| Editors | Michael B. Matthews |
| Publisher | IEEE Computer Society |
| Pages | 485-489 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781665458283 |
| DOIs | |
| State | Published - 2021 |
| Event | 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 - Virtual, Pacific Grove, United States Duration: Oct 31 2021 → Nov 3 2021 |
Publication series
| Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
|---|---|
| Volume | 2021-October |
| ISSN (Print) | 1058-6393 |
Conference
| Conference | 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 |
|---|---|
| Country/Territory | United States |
| City | Virtual, Pacific Grove |
| Period | 10/31/21 → 11/3/21 |
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.