The causal system is more practical then the noncausal system in the world. Causality implies only the past input can effect the future output. As a consequence, noncausal system is seldom investigation. The purpose of this thesis is to study the signal recury for a noncausal system. The principle of signal estimation is based upon the Wiener-Hopf equation. Therefore, the correlation computation is very important. By transforming the noncausal dynamic equations to a causal equation, we achieve a partial recursive computation structure for correlation computation. However the current input is not independent of the past signal in the noncausal system. Hence, the Mason Rule is applied to solved this problem to make the above recursive structure complete. Furthermore, a recursive computation of Mason Rule for stage propagation is developed in this thesis to accelerating the processing speed. Our algorithm is applied to image restoration. We first segment the image to find the required generating input ponen for each correlated region. Secondly, we extend our 1-D algorithms to 2-D algorithm to restore the image. Our method is compared with the method developed base upon the Gaussian Markov model. The experiments results demonstrate the advantage of method in both visual quailty and numerical results.