The conjugate gradient regularization method in computed tomography problems
The authors solve inverse problems coming from the area of computed tomography by means of regularization methods based on conjugate gradient iterations. They 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. They report computational experiments based on different physical models and with different degrees of noise. The authors compare the results obtained with those computed by other currently used methods such as algebraic reconstruction techniques and back projection.
scientific article; zbMATH DE number 1311060