An Adaptive Primal-Dual Method for CT Image Reconstruction

This paper examines the challenge of Computed Tomography (CT) image reconstruction problem from incomplete projection data. Based on the underdetermined system of equations, we incorporate a Total Variation (TV) regularization term and data fidelity term to formulate a TV-CT model for image reconstruction. We present a comprehensive derivation of the primal-dual method to solve the corresponding saddle point problem, as well as the primal-dual algorithms. Particularly, we introduce adaptive stepsize strategies for the proposed primal-dual algorithm to enhance the reconstruction performance. Finally, numerical experiments are conducted to verify the proposed method, including comparisons with state-of-the-art methods.