An Adaptive Regularization Iterative Reconstruction Algorithm on the Basis of a Sparse Constraint

In this paper, the constraints on Weighting of ASD-POCS (Adaptive Steepest Descent-Projection Onto Convex Sets, ASD-POCS) algorithm weight caused by the variability of different applications, algorithm robustness and poor. It proposes an adaptive regularization iterative reconstruction algorithm on the basis of a sparse constraint: the AR-SART-CG (Adaptive Regularization-Simultaneous Algebraic Reconstruction Techniques-Conjugate Gradient, AR-SART-CG) algorithm. The algorithm adopts a kind of Lagendijk type of Regularization Strategy to construct optimization, respectively uses local variance, noise estimation, and image energy estimation to adaptively adjust the parameters weighted diagonal matrix and global regularization, and respectively applies SART algorithm and conjugate gradient method to solve optimizations of fidelity term and constraint term. Since that the algorithm can adaptively adjust the weight of constraints, it is possessed of strong robustness. The experimental results show that the AR-SART-CG algorithm can better balance and preserve the relations between picture edge and smooth noise.