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Real-Time Deconvolution with GPU and Spark for Big Imaging Data Analysis

  • Lianyu CaoEmail author
  • Penghui Juan
  • Yinghua Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9530)

Abstract

Light sheet fluorescence microscopy (LSFM) led researchers to get optical sections of large samples, virtually without toxicity and light bleaching and with high temporal resolution, and to record the development of large, living samples with exceptionally high information content. And images observed by LSFM with high signal to noise ratio are very suited for three-dimensional reconstruction. Deconvolution reduces blurring from out-of-focus light to improve the contrast and sharpness of image, but commercial deconvolution software is slow and expensive which cannot meet the current demand. GPU is the new many-core processor with powerful floating point performance, so we parallelized the Richardson Lucy Deconvolution on the GPU. Under ensuring image quality, the implementation on the GPU runs ~30 times faster than the implementation on the CPU. For an image of size 1024 × 1024 × 25, the deconvolved time of 50 iterations on the GPU is no more than 2 s.

Keywords

Deconvolution GPU Spark Manycore Imaging 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

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