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This paper compares the steady-state reduced update Kalman filter to the unrealizable Wiener filter for the two-dimensional LMMSE estimation of imaqes and random fields. The comparison is composed of three parts: experimental MSE performance, subjective quality of the estimates, and computational complexity. The performance comparison is conducted on both real and synthetic image data. The Wiener filters are designed using both estimated power density spectra and the AR models necessary for the Kalman filter. These AR models are determined using 2-D linear prediction techniques on real image data. The computational comparison considers both multiplies and adds as well as amount and type of required memory.