Real-time multi-view deconvolution
Author(s) -
Benjamin Schmid,
Jan Huisken
Publication year - 2015
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btv387
Subject(s) - deconvolution , computer science , massively parallel , cuda , bottleneck , graphics processing unit , source code , code (set theory) , pipeline (software) , computer graphics (images) , blind deconvolution , computational science , artificial intelligence , computer vision , parallel computing , algorithm , set (abstract data type) , programming language , embedded system , operating system
In light-sheet microscopy, overall image content and resolution are improved by acquiring and fusing multiple views of the sample from different directions. State-of-the-art multi-view (MV) deconvolution simultaneously fuses and deconvolves the images in 3D, but processing takes a multiple of the acquisition time and constitutes the bottleneck in the imaging pipeline. Here, we show that MV deconvolution in 3D can finally be achieved in real-time by processing cross-sectional planes individually on the massively parallel architecture of a graphics processing unit (GPU). Our approximation is valid in the typical case where the rotation axis lies in the imaging plane.
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