The deblurring of the satellite photograph for the Air Force, for example, required sixteen computers working on digitized images of the Hubble Spacecraft. Schulz knows that the light spreads in a very complicated way over a large region. If he knows something about how the light spreads, he can start manipulating the numbers to get them back to where they should have been in the first place. A major difficulty in the problems Schulz addresses is that these point-spread functions have to be determined from the data. Image restoration is only one part of image processing.
Signal processing is used to actually construct an image that can then be interpreted.
The image processor needs to understand how the data was collected, what information needs to be extracted, and how it needs to be presented. Quantifying the Elusive All image processing work requires an understanding of statistical modeling, optics, basic probability and random-process theory, and estimation theory. The problems are mathematical, and Schulz likes to address them with classical methods of statistical inference. As with everything, there are right ways and wrong ways to do things. One of the frustrations of solving signal processing problems is that an incorrect process can be used, and an improved image could still result.