A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Choice of Regularisation Parameter in Image Restoration
Proceedings of the 4th International Conference on Pattern Recognition
A generalized Landweber iteration for ill-conditioned signal restoration
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
The Application of Constrained Least Squares Estimation to Image Restoration by Digital Computer
IEEE Transactions on Computers
A regularized iterative image restoration algorithm
IEEE Transactions on Signal Processing
Prototype image constraints for set-theoretic image restoration
IEEE Transactions on Signal Processing
Blur identification by the method of generalized cross-validation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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The restoration problem deals with images in which information has been destroyed or obscured. In this paper, we present a framework for addressing image restoration problems in which the goal is to recover information about the image. Restoration algorithms often use tentative assumptions to compensate for the information lost in the degradation process. We propose cross-validation as a method for testing such assumptions. Viewed in this way, cross-validation is capable of addressing a number of key image restoration problems. We discuss the various options available for defining and evaluating the cross-validation criterion. Furthermore, we survey recent developments concerning cross-validation in image restoration and demonstrate the power of cross-validation in addressing several image restoration problems-regularization parameter estimation, blur identification, constraint assessment, and an optimal stopping rule for iterative restoration.