Share Email Print

Proceedings Paper

Practical stopping rule for iterative image restoration
Author(s): Stanley J. Reeves; Kevin M. Perry
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Iterative techniques for image restoration are flexible and easy to implement. The major drawback of iterative image restoration is that the algorithms are often slow in converging to a solution, and the convergence point is not always the best estimate of the original image. Ideally, the restoration process should stop when the restored image is as close to the original image as possible. Unfortunately, the original image is unknown, and therefore no explicit fidelity criterion can be computed. The generalized cross-validation (GCV) criterion performs well as a regularization parameter estimator, and stopping an iterative restoration algorithm before convergence can be viewed as a form of regularization. Therefore, we have applied GCV to the problem of determining the optimal stopping point in iterative restoration. Unfortunately, evaluation of the GCV criterion is computationally expensive. Thus, we use a computationally efficient estimate of the GCV criterion after each iteration as a measure of the progress of the restoration. Our experiments indicate that this estimate of the GCV criterion works well as a stopping rule for iterative image restoration.

Paper Details

Date Published: 19 May 1992
PDF: 9 pages
Proc. SPIE 1657, Image Processing Algorithms and Techniques III, (19 May 1992); doi: 10.1117/12.58327
Show Author Affiliations
Stanley J. Reeves, Auburn Univ. (United States)
Kevin M. Perry, Auburn Univ. (United States)

Published in SPIE Proceedings Vol. 1657:
Image Processing Algorithms and Techniques III
James R. Sullivan; Benjamin M. Dawson; Majid Rabbani, Editor(s)

© SPIE. Terms of Use
Back to Top