Parallel statistical multiresolution estimation for image reconstruction.

Kramer, S. C.; Hagemann, J.; Kunneke, L.; Lebert, J.

doi:10.1137/15M1020332

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Parallel statistical multiresolution estimation for image reconstruction.

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Kramer, S. C.
Emeritus Group Laboratory of Cellular Dynamics, MPI for Biophysical Chemistry, Max Planck Society;

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Citation

Kramer, S. C., Hagemann, J., Kunneke, L., & Lebert, J. (2016). Parallel statistical multiresolution estimation for image reconstruction. SIAM Journal on Scientific Computing, 38(5), C533-C559. doi:10.1137/15M1020332.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002C-117B-9

Abstract

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.