From the results presented here, it is apparent that the set-theoretic based iterative restoration algorithms provide very good tools for the HST data restoration problem. We have shown that these algorithms are not dependent on any a priori knowledge of the signal and noise variances present. For the generalized iterative adaptive choice of the general regularization parameter, § 2.1., there is also no dependency on the initial conditions of the iteration. In addition, the algorithms are relatively fast. This is especially true of the frequency adaptive algorithm which generally converges in a small number iterations. Given these considerations, the algorithms discussed in this paper present a very good alternative to some of the standard approaches being applied to HST data currently.