Optimal space-varying regularization in iterative image restoration
- 1 May 1994
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 3 (3) , 319-324
- https://doi.org/10.1109/83.287028
Abstract
It has been shown that space-variant regularization in image restoration provides better results than space-invariant regularization. However, the optimal choice of the regularization parameter is usually unknown a priori. In previous work, the generalized cross-validation (GCV) criterion was shown to provide accurate estimates of the optimal regularization parameter. The author introduces a modified form of the GCV criterion that incorporates space-variant regularization and data error terms. Furthermore, he presents an efficient method for estimating the GCV criterion for the space-variant case using iterative image restoration techniques. This method performs nearly as well as the exact criterion for the image restoration problem. In addition, he proposes a Wiener filter interpretation for choosing the local weighting of the regularization. This interpretation suggests the use of a multistage estimation procedure to estimate the optimal choice of the local regularization weights. Experiments confirm the value of the modified GCV estimation criterion as well as the multistage procedure for estimating the local regularization weightsKeywords
This publication has 9 references indexed in Scilit:
- Nonstationary iterative image restorationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Optimal regularization parameter estimation for image restorationPublished by SPIE-Intl Soc Optical Eng ,1991
- Cross-validation and other criteria for estimating the regularizing parameterPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- Optimal estimation of the regularization parameter and stabilizing functional for regularized image restorationOptical Engineering, 1990
- Simultaneous Blur Identification And Image Restoration Using The EM AlgorithmPublished by SPIE-Intl Soc Optical Eng ,1989
- A fast ?Monte-Carlo cross-validation? procedure for large least squares problems with noisy dataNumerische Mathematik, 1989
- A Stochastic Estimator of the Trace of the Influence Matrix for Laplacian Smoothing SplinesCommunications in Statistics - Simulation and Computation, 1989
- Regularized iterative image restoration with ringing reductionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
- The Application of Constrained Least Squares Estimation to Image Restoration by Digital ComputerIEEE Transactions on Computers, 1973