Abel Transformation Based Adaptive Regularization Approach for Image Reconstruction
DU Jian-peng
LIANG Hai-xia
WEI Su-hua
Abstract：In this paper, we discuss an adaptive regularization approach for density reconstruction of axially symmetric object whose tomography comes from a single X-ray projection. The method we proposed is based on the combination of total variation regularization and high-order total variation regularization. Its main advantage is to reduce the staircase effect while keeping sharp edges and recovering smoothly varying regions. Moreover, it simplifies the use of parameters. We apply the augmented Lagrangian method to solve the optimization involved. Numerical results show that the proposed method has improved the accuracy of density edges and values. Besides, the method is not sensitive to the measured data noise.
Keywords：CTadaptivehigh-order total variation regularizationaugmented Lagrangian methodAbel inversion
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 435-445 )