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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Santi, P.A.: Light Sheet fluorescence microscopy a review. J Histochem Cytochem 59(2), 129–138 (2011). doi:10.1369/0022155410394857
Reynaud, E.G., Krzic, U., Greger, K., Stelzer, E.H.K.: Light sheet-based fluorescence microscopy: more dimensions, more photons, and less photodamage. HFSF J 2(5), 266–275 (2008)
Shaw, P.J.: Comparison of wide-field/deconvolution and confocal microscopy for 3D imaging. In: Pawley, J.B. (ed.) Handbook of Biological Confocal Microscopy, pp. 373–387. Springer, Heidelberg (1995)
Apache Spark. https://spark.apache.org/
NVIDIA CUDA. http://www.nvidia.com/object/cuda_home_new.html
Zaharia, M., Chowdhury, M., Franklin, M.J.: Spark: cluster computing with working sets (2010)
Richardson, W.H.: Bayesian-base iterative method of image restoration. JOSA 62(1), 55–59 (1972)
Lucy, L.B.: An iterative technique for the rectification of observed distributions. Astron. J. 79, 745–754 (1974)
Richardson Lucy: http://en.wikipedia.org/wiki/RichardsonLucy_deconvolution
NVIDIA CUDA C Programming Guide. http://www.NVIDIA.com/cuda
Ryoo, S., Rodrigues, C.I., Baghsorkhi, S.S.: Optimization principles and application performance evaluation of a multithreaded GPU using CUDA. In: Proceedings of the 13th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (2008)
Spark Programming Guide. http://spark.apache.org/docs/latest/programming-guide.html
FFTW. http://www.fftw.org
Griffa, A., Garin, N., Sage, D.: Hollow Bars. http://bigwww.epfl.ch/deconvolution/?p=bars
ImageJ. http://imagej.nih.gov/ij/
Ingaramo, M., York, A.G., Hoogendoorn, E., Postma, M., Shroff, H., Patterson, G.H.: Richardson-Lucy deconvolution as a general tool for combining images with complementary strengths. ChemPhySchem 15(4), 794–800 (2014)
Liu, H., Zhang, Z., Liu, S., Liu, T., Yan, L., Zhang, T.: Richardson-Lucy blind deconvolution of spectroscopic data with wavelet regularization. Appl. Optics 54(7), 1770–1775 (2015)
Stohl, F., Kaminski, C.F.: A joint Richardson—Lucy deconvolution algorithm for the reconstruction of multifocal structured illumination microscopy data. Methods Appl. Fluoresc. 3(1), 014002 (2015)
Zhu, J., Chen, L., Chen, A., Luo, G., Deng, X., Liu, X.: Fast 3D dosimetric verifications based on an electronic portal imaging device using a GPU calculation engine (2015)
Sherry, M., Shearer, A.: IMPAIR: massively parallel deconvolution on the GPU. In: Proceedings of SPIE - The International Society for Optical Engineering (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Cao, L., Juan, P., Zhang, Y. (2015). Real-Time Deconvolution with GPU and Spark for Big Imaging Data Analysis. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9530. Springer, Cham. https://doi.org/10.1007/978-3-319-27137-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-27137-8_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27136-1
Online ISBN: 978-3-319-27137-8
eBook Packages: Computer ScienceComputer Science (R0)