Sparse-View CT Reconstruction Based on Nonconvex L1 − L2 Regularizations
International Scholarly and Scientific Research & Innovation
First page number:
Last page number:
We present a nonconvex, Lipschitz continuous and non-smooth regularization model. The CT reconstruction is formulated as a nonconvex constrained L1 − L2 minimization problem and solved through a difference of convex algorithm and alternating direction of multiplier method which generates a better result than L0 or L1 regularizers in the CT reconstruction.
Computed tomography, sparse-view reconstruction, L1 −L2 minimization, non-convex, difference of convex functions
Yazdanpanah, A. P.,
Sparse-View CT Reconstruction Based on Nonconvex L1 − L2 Regularizations.
International Scholarly and Scientific Research & Innovation, 11(4),