Share Email Print

Proceedings Paper

Estimation risk of transformation-averaged estimators
Author(s): Juan Liu; Pierre Moulin
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Wavelet image denoising practice has shown that the performance of simple estimators may be substantially improved by averaging these estimators over a collection of transformations such as translations or rotations. In this paper, we explain and quantify these empirical findings using estimation theory. We consider a general nonlinear observation model, analyze the estimation risk of transformation-averaged estimators, and derive an upper bound on the risk reduction due to transformation averaging. The bound is evaluated for several estimators, using different averaging strategies (including a randomized strategy) and different wavelet bases. The practical usefulness of the bound is established for standard image denoising examples.

Paper Details

Date Published: 13 November 2003
PDF: 10 pages
Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); doi: 10.1117/12.508038
Show Author Affiliations
Juan Liu, Palo Alto Research Ctr. (United States)
Pierre Moulin, Univ. of Illinois/Urbana-Champaign (United States)

Published in SPIE Proceedings Vol. 5207:
Wavelets: Applications in Signal and Image Processing X
Michael A. Unser; Akram Aldroubi; Andrew F. Laine, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?