Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing
A lecture about challenges and solutions for GPU-accelerating bio-image analysis workflows and running them in the cloud.
A lecture about challenges and solutions for GPU-accelerating bio-image analysis workflows and running them in the cloud.
This chapter is part of this book. The 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.
This lesson shows how to use Python and skimage to do basic image processing.
This is a resource for image analysis training material, with a focus on research in the life sciences.
Currently, this resource is mainly meant to serve image analysis trainers, helping them to design courses. However, we might add more text (or videos) to the material such that it could also be used by students for self-directed study.