Galatsanos The research is exploring a novel, multi-channel approach to the image restoration problem that utilizes a wavelet-based subband decomposition. Such a decomposition facilitates the incorporation of localized space-variant statistics and prior knowledge into the restoration process as well as adaptive noise suppression at various resolution levels. Furthermore, it can be implemented in a practical and computationally efficient manner. In order to fully exploit the potential advantages of this approach, three classes of multi-channel restoration algorithms are being considered: 1. stochastic restoration algorithms where the multi-channel statistics of the wavelet-based subbands are used to model the space-varying nature of the original image; 2. deterministic algorithms where multi-channel regularization operators and parameters are used; and 3. hierarchical image restoration where the image is progressively restored starting from its coarse features and continuing to the finer ones.