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DTSTART;TZID=Europe/Stockholm:20230628T110000
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UID:submissions.pasc-conference.org_PASC23_sess121_msa203@linklings.com
SUMMARY:Pycsou: High-Performance Computational Imaging with Python
DESCRIPTION:Minisymposium\n\nMatthieu Simeoni (EPFL)\n\nDeveloping high-qu
 ality 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 acceler
 ate inference tasks. The wide range of skills required poses a major barri
 er to the adoption of advanced imaging solutions in production pipelines. 
 In this presentation we introduce Pycsou, an open-source computational ima
 ging software framework which directly addresses this issue. This framewor
 k allows imaging scientists at any level to easily prototype imaging pipel
 ines 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 t
 ools (HPC) from the PyData stack:<br />- Native support for distributed an
 d out-of-core computing on CPUs/GPUs.<br />- A precision context manager f
 or changing locally the compute precision.<br />- Just-in-time compilation
  via Numba of compute-critical components.<br />- Vectorized operators to 
 efficiently process multiple inputs in parallel.<br /> To illustrate the p
 erformance 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.\n\nDomain: Computer Sci
 ence, Machine Learning, and Applied Mathematics &#8232;\n\nSession Chair: Matthi
 eu Simeoni (EPFL)
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