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Content Provider | IEEE Xplore Digital Library |
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Author | Kromwijk, Sander Lefkimmiatis, Stamatios Unser, Michael |
Copyright Year | 2014 |
Description | Author affiliation: Biomedical Imaging Group, EPFL, CH-1015 Lausanne, Switzerland (Kromwijk, Sander; Lefkimmiatis, Stamatios; Unser, Michael) |
Abstract | In this work, we describe our approach of combining the most effective ideas and tools developed during the past years to build a variational 3D deconvolution system that can be successfully employed in fluorescence microscopy. In particular, the main components of our deconvolution system involve proper handling of image boundaries, choice of a regularizer that is best suited to biological images, and use of an optimization algorithm that can be efficiently implemented on graphics processing units (GPUs) and fully benefit from their massive parallel computational capabilities. We show that our system leads to very competitive results and reduces the computational time by at least one order of magnitude compared to a CPU implementation. This makes the use of advanced deconvolution techniques feasible in practice and attractive computationally. |
Starting Page | 1718 |
Ending Page | 1722 |
File Size | 300364 |
Page Count | 5 |
File Format | |
ISBN | 9781479957514 |
DOI | 10.1109/ICIP.2014.7025344 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2014-10-27 |
Publisher Place | France |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Graphics processing units Deconvolution Three-dimensional displays Image reconstruction Microscopy Symmetric matrices Distortion measurement convex optimization Graphics Processing Unit image regularization variational reconstruction |
Content Type | Text |
Resource Type | Article |
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