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.