CLIJ

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GPU-accelerated image processing in ImageJ using CLIJ

View the Project on GitHub clij/clij-docs

CLIJ: GPU-accelerated image processing for everyone

Introduction

CLIJ is an OpenCL - ImageJ bridge and a Fiji plugin allowing users with entry-level skills in programming to build GPU-accelerated workflows to speed up their image processing. Increased efforts were put on documentation, code examples, interoperability, and extensibility. CLIJ is based on ClearCL, Imglib2, ImageJ and SciJava.

If you use CLIJ, please cite it:

Robert Haase, Loic Alain Royer, Peter Steinbach, Deborah Schmidt, Alexandr Dibrov, Uwe Schmidt, Martin Weigert, Nicola Maghelli, Pavel Tomancak, Florian Jug, Eugene W Myers. CLIJ: GPU-accelerated image processing for everyone. BioRxiv preprint. https://doi.org/10.1101/660704

If you search for support, please open a thread on the image.sc forum.

Image.sc forum

Overview

Acknowledgements

Development of CLIJ is a community effort. We would like to thank everybody who helped developing and testing. In particular thanks goes to Alex Herbert (University of Sussex), Brenton Cavanagh (RCSI), Brian Northan (True North Intelligent Algorithms), Bruno C. Vellutini (MPI CBG), Curtis Rueden (UW-Madison LOCI), Damir Krunic (DKFZ), Daniel J. White (GE), Gaby G. Martins (IGC), Siân Culley (LMCB MRC), Giovanni Cardone (MPI Biochem), Jan Brocher (Biovoxxel), Johannes Girstmair (MPI CBG), Juergen Gluch (Fraunhofer IKTS), Kota Miura, Laurent Thomas (Acquifer), Nico Stuurman (UCSF), Peter Haub, Pete Bankhead (University of Edinburgh), Pradeep Rajasekhar (Monash University), Tanner Fadero (UNC-Chapel Hill), Thomas Irmer (Zeiss), Tobias Pietzsch (MPI-CBG), Wilson Adams (VU Biophotonics)