Parallel statistical multiresolution estimation for image reconstruction

We show that a careful parallelization of statistical multiresolution estimation (SMRE) improves the phase reconstruction in X-ray near-field holography. The central step in, and the computationally most expensive part of, SMRE methods is Dykstra's algorithm. It projects a given vector onto the intersection of convex sets. We discuss its implementation on NVIDIA's compute unified device architecture (CUDA). Compared to a CPU implementation parallelized with OpenMP, our CUDA implementation is up to one order of magnitude faster. Our results show that a careful parallelization of Dykstra's algorithm enables its use in large-scale statistical multiresolution analyses.