Digital image restoration requires some knowledge of the degradation phenomena in order to attempt an inversion of that degradation. Typically, degradations which are included in the restoration process are those resulting from the optics and electronics of the imaging device. Occasionally, blurring caused by an intervening atmosphere, uniform motion or defocused optics is also included. Recently it has been shown that sampling, the conversion of the continuous output of an imaging system to a discrete array, further degrades or blurs the image. Thus, incorporating sampling effects into the restoration should improve the quality of the restored image. The system transfer function (the Fourier transform of the point spread function), was derived for the Landsat Multi-Spectral Scanner (MSS) and Thematic Mapper (TM) systems. Sampling effects were included, along with the relevant optical, instantaneous-field-of-view and electronic filter data, in the system analysis. Using the system transfer function, a least-squares (Wiener) filter was derived. A Wiener filter requires the ratio of the power spectra of the scene and noise, which is often, for simplicity, assumed to be a constant over frequency. Our restoration includes models for the power spectra which are based on the study of several different types of Landsat scenes. The Wiener filter is then inverse Fourier transformed to find a restoration filter which is spatially windowed to suppress ringing. Visual evaluations are made of the restored imagery.