The strategies for image reconstruction from optical synthetic-aperture data can be divided into two camps according to their historical legacies: those coming from the field of more conventional optical image processing, where the data are relatively complete in terms of Fourier coverage, have concentrated on trying to recover images using “brute force” methods. They make use of the massive size of the typical datasets to try and derive an image without having to use any a-priori constraints. The size of the datasets to some extent restricts the algorithms used towards being as simple and hence fast as possible. Radio-astronomical imaging strategies, on the other hand, have always been designed to cope with sparse and missing data, and many sophisticated algorithms have been designed to make maximum use of a-priori constraints with iterative global fitting techniques.