Abstract
Title : Total Variation Based Fourier Reconstruction and Regularization for Computer Tomography
Authors : Xiao-Qun Zhang and Jacques Froment
The paper develops a tomographic reconstruction and regularization
method based on a total variation minimization constrained by the
knowledge of the input intervals the Fourier coefficients belong
to. Experiments show that the approach outperforms classical
reconstruction methods such as direct Fourier method (DFM), filtered
back-projection (FBP) and Tikhonov iterative method (TIM), both
in terms of PSNR (an objective mean-square error) and visual quality,
especially in the case of noisy or sparse data. In addition the
resulting algorithm requires a number of operations of $O(N^2\log N)$
only, and is therefore faster than ordinary iterative methods, such as
space-based TIM.