Image generating system is usually considered as a noncausal system. The Kalman filter and the Wiener filter are two important linear filters for signal estimation. They are developed for the causal signal and noncausal signal respectively. However, the Kalman filter can also be applied to the noncausal system by rewriting the signal generating equation. In this thesis, we study the performance of the Wiener filter and the Kalman filter applied to image restoration. Our experiments have demonstrated that the rank of list for error performance is: the full order Winner filter, the Kalman filter, the reduced Kalman filter, the three-order Wiener filter. This performance is consisted with the amount of data used in the linear estimation. On the other hand the list for computation performance is as following: the reduced Kalman filter, the three-order Wiener filter, the Kalman filter, the full order Wiener filter. The efficiency of the reduced Kalman filter can be understood by the computation saving of huge updating procedures. It should be noted that the efficiency of applying the regular Kalman filter in this thesis is achieved by fully employed the special form of system matrix involved. In addition to the above noncausal image model, a causal image model can also be built if the central pixel is assumed to be affected only by the left and the upper pixels. The second model is not natural but is obviously advantageous in computation efficiency compared to the first model. However, the first model is much better than the second model error performance. Therefore, it is suggested that the natural image should be modeled as a noncausal model.