CLIJ - GPU-accelerated image processing in ImageJ

Introduction

CLIJ is an OpenCL - ImageJ bridge and a Fiji plugin allowing users with no computer science background to explort GPU-acceleration for their image processing workflows. Increased efforts were put on documentation, code examples, interoperability, extensibility and user-convenience. CLIJ is based on ClearCL, Imglib2, ImageJ and SciJava

If you use CLIJ, please cite it:

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

Overview

• CLIJ - a quick tour
• Example benchmarks
• Installation
  • Fiji update site
  • Depending on CLIJ via maven
• Introduction to CLIJ programming
  • CLIJ for ImageJ Macro programmers
  • CLIJ for Java programmers
  • ImageJ Jupyter notebooks in Groovy
  • Execution from the command line
• Application programming interface (API)
  • API design principles
  • ImageJ Macro
  • Jython
  • Java
  • Groovy
• Code examples
  • ImageJ Macro
  • BeanShell
  • Jython
  • JavaScript
  • Groovy
  • Java
• Extending CLIJ functionality
  • Plugin template
  • Example plugin for convolution/deconvolution
• Troubleshooting
  • List of tested systems
  • Troubleshooting
  • Support

Acknowledgements

Development of CLIJ is a community effort. We would like to thank everybody who helped developing and testing. In particular thanks goes to Bruno C. Vellutini (MPI CBG), Curtis Rueden (UW-Madison LOCI), Damir Krunic (DKFZ), Daniel J. White (GE), Gaby G. Martins (IGC), Giovanni Cardone (MPI Biochem), Jan Brocher (Biovoxxel), Johannes Girstmair (MPI CBG), Juergen Gluch (Fraunhofer IKTS), Kota Miura (Heidelberg), Laurent Thomas (Acquifer), Nico Stuurman (UCSF), Pavel Tomancak (MPI CBG), Pradeep Rajasekhar (Monash University), Tobias Pietzsch (MPI-CBG)

TODO:

• Remove code examples from clij_
• Remove documentation from clij_, link here
• Update links/intro on clij-notebook
• Finish benchmarking page
• Put Drosophila data sets for benchmarking online
• Add link to BioRxiv