Foveated, log­polar, or space­variant image architectures provide a high 
resolution and wide field workspace, while providing a small pixel computation 
load. These characteristics are ideal for mobile robotic and active vision 
applications. A common problem in these application areas is image blur and 
motion artifact. Recently, there has been described a generalization of the 
Fourier Transform (the fast exponential chirp transform) which allows 
frame­rate computation of full­field 2D frequency transforms on a log­polar 
image format. In the present work, we use Wiener filtering, performed using 
the Exponential Chirp Transform, on log­polar (foveated) image formats to 
de­blur images which have been degraded by camera motion or other sources of 
blur. Since the exponential chirp transform provides size and rotation 
invariant properties, we only need to pre­compute and store a single motion 
vector template, exploiting the size and rotation properties of the chirp 
transform. This provides real­time performance without requiring a large 
storage of pre­computed templates. Examples of this processing on different 
samples from a data base of images shows effective image restoration, with 
frame­rate performance on modest hardware (e.g. a Pentium or SHARC DSP 
processor). This work shows that the fast exponential chirp transform provides 
a simple algorithm with low spatial complexity for full frame, real­time image
restoration of foveal imagery.