| Abstract: | 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. |
