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  Parallel statistical multiresolution estimation for image reconstruction.

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-117B-9 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-117D-5
Genre: Journal Article

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 Creators:
Kramer, S. C.1, Author              
Hagemann, J., Author
Kunneke, L., Author
Lebert, J., Author
Affiliations:
1Emeritus Group Laboratory of Cellular Dynamics, MPI for Biophysical Chemistry, Max Planck Society, ou_578629              

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 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.

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Language(s): eng - English
 Dates: 2016-10-112016
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1137/15M1020332
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Title: SIAM Journal on Scientific Computing
Source Genre: Journal
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Pages: - Volume / Issue: 38 (5) Sequence Number: - Start / End Page: C533 - C559 Identifier: -