GPU-accelerating ImageJ Macro image processing workflows using CLIJ

TL;DR

This paper introduces methods to accelerate ImageJ macro image processing workflows by leveraging GPU computing through CLIJ, providing a tutorial for translating existing macros into GPU-accelerated versions.

Contribution

It presents a step-by-step tutorial for converting ImageJ macros into GPU-accelerated macros using CLIJ, enhancing performance in image processing workflows.

Findings

GPU acceleration significantly improves processing speed.

Guidelines enable easy translation of macros to GPU-accelerated versions.

Tutorial facilitates adoption of GPU computing in ImageJ workflows.

Abstract

This chapter introduces GPU-accelerated image processing in ImageJ/FIJI. The reader is expected to have some pre-existing knowledge of ImageJ Macro programming. Core concepts such as variables, for-loops, and functions are essential. The chapter provides basic guidelines for improved performance in typical image processing workflows. We present in a step-by-step tutorial how to translate a pre-existing ImageJ macro into a GPU-accelerated macro.