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The conjugate gradient regularization method in Computed Tomography problems
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DOI：10.1016/S0096-3003(98)10007-3.png)
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Abstract
En 中文
In this work we solve inverse problems coming from the area of Computed Tomography by means of regularization methods based on conjugate gradient iterations. We develop a stopping criterion which is efficient for the computation of a regularized solution for the least-squares normal equations. The stopping rule can be suitably applied also to the Tikhonov regularization method. We report computational experiments based on different physical models and with different degrees of noise. We compare the results obtained with those computed by other currently used methods such as Algebraic Reconstruction Techniques (ART) and Backprojection. (C) 1999 Elsevier Science Inc. All rights reserved.
Keywords:
ILL-POSED PROBLEMS
LEAST-SQUARES
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