Introduction

Many of the presentations at this workshop have, quite properly, centered on problems in image restoration - mathematical underpinnings of algorithms, proper image bases for reconstruction, biases in measurement from deconvolved images. These might leave a depressing view of the prospects for getting scientific results from deconvolution, but the opposite is more nearly the case. I will review here some experience as to when image restoration is the right tool for the job, and describe some of the significant science that has come from analysis of deconvolved HST imagery.

Deconvolution is clearly a powerful analysis tool. The astronomical community's experience with HST and IRAS data has started to spread expertise in use of deconvolution techniques beyond its traditional base in aperture synthesis, and is leading to a more sophisticated view of data and measurement than was needed to interpret images blurred by the nearly Gaussian PSF of long-exposure images from the ground. In retrospect, we made it so long without them because eye, brain, and mathematics can deal with this Gaussian-like PSF very naturally; in many cases, we didn't even realize what visual processing was going on! Deconvolution algorithms are especially potent for astronomers as part of a software toolkit including image modelling, convolution, noise tracking, and measurement routines.