This tutorial paper discusses the use of successive-approximation-based iterative restoration algorithms for the removal of linear blurs and noise from images. Iterative algorithms are particularly attractive for this application because they allow for the incorporation of prior knowledge about the class of feasible solutions, because they can be used to remove nonstationary blurs, and because they are fairly robust with respect to errors in the approximation of the blurring operator. Regularization is introduced as a means for preventing the excessive noise magnification that is typically associated with ill-posed inverse problems such as the deblurring problem. Iterative algorithms with higher convergence rates and a multistep iterative algorithm are also discussed. A number of examples are presented.