Open AccessTwo plus one is almost three: a fast approximation for multi-view deconvolution
Author(s) -
Manuel Hüpfel,
Manuel Fernández Merino,
Johannes Bennemann,
Masanari Takamiya,
Sepand Rastegar,
Anja Tursch,
Thomas W. Holstein,
G. Ulrich Nienhaus
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.443660
Subject(s) - deconvolution , point spread function , computer science , blind deconvolution , image processing , algorithm , wiener deconvolution , rotation (mathematics) , computation , computer vision , isotropy , artificial intelligence , optics , image (mathematics) , physics
Multi-view deconvolution is a powerful image-processing tool for light sheet fluorescence microscopy, providing isotropic resolution and enhancing the image content. However, performing these calculations on large datasets is computationally demanding and time-consuming even on high-end workstations. Especially in long-time measurements on developing animals, huge amounts of image data are acquired. To keep them manageable, redundancies should be removed right after image acquisition. To this end, we report a fast approximation to three-dimensional multi-view deconvolution, denoted 2D+1D multi-view deconvolution, which is able to keep up with the data flow. It first operates on the two dimensions perpendicular and subsequently on the one parallel to the rotation axis, exploiting the rotational symmetry of the point spread function along the rotation axis. We validated our algorithm and evaluated it quantitatively against two-dimensional and three-dimensional multi-view deconvolution using simulated and real image data. 2D+1D multi-view deconvolution takes similar computation time but performs markedly better than the two-dimensional approximation only. Therefore, it will be most useful for image processing in time-critical applications, where the full 3D multi-view deconvolution cannot keep up with the data flow.