Hierarchical approach to image estimation

In general, images are inhomogeneous and no single model can accurately represent all the N×N data points of an image. Thus the linear space-invariant (LSI) filter can not produce the best estimates, especially at the lower SNR´s. In fact, LSI filters tend to smooth the edges excessively when estimating undistorted images corrupted by additive white Gaussian noise. If we transform the original image space to a more appropriate space, and then process images in the new space, we may obtain better visual quality and lower numeric error also. Investigating such a transformation is the main concept of this paper.