Developments in the theory of image reconstruction and restoration over the past 20 or 30
years are outlined. Particular attention is paid to common estimation structures and to …

Several aspects of the application of regularization theory in image restoration are
presented. This is accomplished by extending the applicability of the stabilizing functional …

Methods are reviewed for choosing regularized restorations in image processing. In
particular, a method developed by Galatsanos and Katsaggelos (see ibid., vol. 1, p. 322 …

The authors develop an iterative, adaptive, space-variant restoration incorporating both
regularization and artifact-suppression constraints. The algorithm is formulated using the …

The feasible solution to the signal restoration problem is defined as the one which satisfies
all constraints which can be imposed on the true solution. A very important set of constraints …

The article introduces digital image restoration to the reader who is just beginning in this
field, and provides a review and analysis for the reader who may already be well-versed in …

While many iterative signal restoration methods have been shown to converge in the
mathematical sense, a practical criterion is needed to indicate when the numerical algorithm …

The development of the algorithm is based on a set theoretic approach to regularization.
Deterministic and/or statistical information about the undistorted image and statistical …

The method of regularization is portrayed as providing a compromise between fidelity to the
data and smoothness, with the tradeoff being determined by a scalar smoothing parameter …

The determination of the regularization parameter is an important issue in regularized image
restoration, since it controls the trade-off between fidelity to the data and smoothness of the …