Abstract
A generalized iterative restoration algorithm for linearly degraded images is presented, based on the singular value decomposition of the degradation operator. Covergence of the algorithm is accelerated by imposing constraints on the solution. Realizations of the restoration algorithm for various types of degradation are presented, and the analogy to a modified version of the method by Gerchberg [ Opt. Acta21, 709 ( 1974)] and Papoulis [ IEEE Trans. Circuits Syst.CAS-22, 735 ( 1975)] is discussed. Results from computer-simulated images are given, and the effects of noise on the restored images are considered. The performance of the algorithm is found to be superior to that of pseudoinverse techniques.
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