Pycsou: High-Performance Computational Imaging with Python
DescriptionDeveloping high-quality computational imaging methods not only requires a deep understanding of the physics underlying an imaging system, but also thorough knowledge of optimisation and computer science to efficiently distribute and accelerate inference tasks. The wide range of skills required poses a major barrier to the adoption of advanced imaging solutions in production pipelines. In this presentation we introduce Pycsou, an open-source computational imaging software framework which directly addresses this issue. This framework allows imaging scientists at any level to easily prototype imaging pipelines by composing fundamental building-blocks in plug-and-play fashion and deploy them in production environments. To achieve excellent performance and scalability, Pycsou leverages a number of high-performance computing tools (HPC) from the PyData stack:
- Native support for distributed and out-of-core computing on CPUs/GPUs.
- A precision context manager for changing locally the compute precision.
- Just-in-time compilation via Numba of compute-critical components.
- Vectorized operators to efficiently process multiple inputs in parallel.
To illustrate the performance and versatility of the software, we will show some examples of state-of-the-art image reconstruction pipelines implemented in Pycsou for both radio-interferometric and biomedical imaging.
TimeWednesday, June 2811:00 - 11:30 CEST
Computer Science, Machine Learning, and Applied Mathematics