Abstract
The aim of image restoration is to make an estimate of a degraded image as good as possible. The Tikhonov (1964) regularization approach has long been utilized for restoring images that are contaminated by noise and are blurred due for example to camera defocusing or linear motion. It is posed as a least-squares approximation problem in the l/sub 2/ space that provides a parameterized tradeoff between accuracy and smoothness of the restored image. Several methods of choosing the regularization parameter and the stabilizing operator are proposed via optimization approach.
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