Chapter 1 covers background material which is assumed in subsequent chapters. The origin of blurring and noise in imaging systems is briefly described, as are some common mathematical models. Some popular local image restoration techniques are explained in detail, and a few important global algorithms are mentioned. Common measures for comparing the quality of restored images are explained. The final sections argue that local filters are adequate for image restoration in many common scenarios.
Chapter 2 describes the Grid Filter approach. The theory of Local Minimum Mean-Squared Error (LMMSE) filter design is reviewed, and the major differences between previous approaches and grid filters are pointed out. Grid filters are then described in detail, with sections devoted to feature selection, the structure of the grid, interpolation techniques, symmetry assumptions and training.
Chapter 3 presents results for additive noise and blurring (superresolution). Two approaches for incorporating information from larger neighborhoods (foveated footprints and hybrid filters) are compared. Several properties of grid filters, such as passing outliers unchanged and filtering speed are illustrated. The amount of training data required for adequate filtering results is determined. The performance of grid filters on several noise models is evaluated.
Chapter 4 summarizes the important properties and limitations of grid filters, and points out some areas for future research.