Practical stopping rule for iterative image restoration

Stanley J. Reeves; Kevin M. Perry

doi:10.1117/12.58327

19 May 1992 Practical stopping rule for iterative image restoration

Stanley J. Reeves, Kevin M. Perry

Proceedings Volume 1657, Image Processing Algorithms and Techniques III; (1992) https://doi.org/10.1117/12.58327
Event: SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, 1992, San Jose, CA, United States

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.

Citation Download Citation

Stanley J. Reeves and Kevin M. Perry "Practical stopping rule for iterative image restoration", Proc. SPIE 1657, Image Processing Algorithms and Techniques III, (19 May 1992); https://doi.org/10.1117/12.58327

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