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LOCATION:Seehorn
DTSTART;TZID=Europe/Stockholm:20230628T110000
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UID:submissions.pasc-conference.org_PASC23_sess121@linklings.com
SUMMARY:MS5E - High Performance Computational Imaging across Scales, Commu
 nities and Modalities
DESCRIPTION:Minisymposium\n\nA recent trend in the design of imaging syste
 ms consists in replacing fixed-function instrumentation by sensor networks
  with multiplexed data streams. Such distributed sensing architectures gen
 erally yield rich measurements allowing for greater adaptivity and perform
 ances. Unlike traditional imaging systems however, the data they produce i
 s seldom directly interpretable as an image and must be processed by compu
 tational imaging algorithms. The restoration process generally relies on p
 owerful and universal image priors promoting specific perceptual or struct
 ural features of natural images. Despite substantial technical progress, c
 omputational imaging suffers from an adoptability crisis in applied scienc
 es. Indeed, most methods proposed in the literature remain at the proof-of
 -concept stage, requiring expert knowledge to tune or use. This represents
  a major roadblock for the field, which experiences significant slowdown i
 n the adoption of state-of-the-art techniques. To accelerate the path from
  research prototyping to production deployment in imaging science, there i
 s hence a strong need to rethink traditional imaging pipelines, with an em
 phasis on scalability (for both CPUs and GPUs), high performance computing
 , and modularity (for customizability). This minisymposium will foster hig
 h performance computational imaging by bringing together various research 
 and open-source software communities and showcasing modern production imag
 ing pipelines, both in research and industrial environments.\n\nAI for Lar
 ge-Scale High-Dynamic Range Image Reconstruction in Radio Astronomy\n\nEnd
 owing modern radio-interferometric (RI) telescopes with an acute vision re
 quires solving large-scale high-dynamic range inverse problems for image f
 ormation. This calls for sophisticated computational imaging algorithms in
 jecting an image model into the observed data. We discuss two AI-based sol
 u...\n\n\nYves Wiaux and Arwa Dabbech (Heriot-Watt University)\n----------
 -----------\nE-Scopics Ultrasound SaaS: App-Based Computerized Ultrasonogr
 aphy for Ultraportable Probes\n\nAt E-Scopics, we aim at democratizing ult
 rasound imaging with a simple concept: providing a minimal ultrasound prob
 e and a host on which nearly all processings are performed. The challenge 
 of hyper-portability, the availability of massive amounts of raw data and 
 the unlimited computing power enabled...\n\n\nAdrien Besson (E-Scopics)\n-
 --------------------\nPycsou: High-Performance Computational Imaging with 
 Python\n\nDeveloping high-quality computational imaging methods not only r
 equires a deep understanding of the physics underlying an imaging system, 
 but also thorough knowledge of optimisation and computer science to effici
 ently distribute and accelerate inference tasks. The wide range of skills 
 required poses...\n\n\nMatthieu Simeoni (EPFL)\n---------------------\nSof
 tware for Practical Analysis of Materials: Image Based Quantification acro
 ss Communities\n\nThe software for practical analysis for materials -- a r
 elatively mature python project developed with modern coding practices -- 
 is rapidly becoming a standard in in the world of experimental 3D mechanic
 s as well as granular mechanics and is used in fields as wide as neuron-sp
 ine imaging to rock me...\n\n\nEdward Andò (EPFL), Olga Stamati (ESRF), an
 d Emmanuel Roubin (Université Grenoble Alpes)\n\nDomain: Computer Science,
  Machine Learning, and Applied Mathematics 
\n\nSession Chair: Matthieu Si
 meoni (EPFL)
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