Technological advances in microscopy enable scientists to image at unprecedented speed and ever-increasing resolution. This is creating a data deluge where the data management and processing cannot keep up with acquisition rates. Inspired by applications in biology, the Adaptive Particle Representation (APR) has been developed to alleviate bottlenecks in the management of large volumetric image data. It works by locally adapting the resolution to the image signal, reducing the number of sample points by up to two orders of magnitude while maintaining image quality via a user-set error threshold. Unlike
traditional compression formats, however, many important image processing algorithms can be adapted to operate natively on the data-reduced representation, reducing both runtimes and memory usage.
I will present the concepts and fundamental theory of the APR, showcase its utility in image compression and give examples of APR-based image processing. I will touch on practical considerations, such as data structures and file formats, and show how you can install our software and try the APR on your own images.