Computer Science, Machine Learning, and Applied Mathematics
TimeWednesday, June 2811:00 - 13:00 CEST
DescriptionA recent trend in the design of imaging systems consists in replacing fixed-function instrumentation by sensor networks with multiplexed data streams. Such distributed sensing architectures generally yield rich measurements allowing for greater adaptivity and performances. Unlike traditional imaging systems however, the data they produce is seldom directly interpretable as an image and must be processed by computational imaging algorithms. The restoration process generally relies on powerful and universal image priors promoting specific perceptual or structural features of natural images. Despite substantial technical progress, computational imaging suffers from an adoptability crisis in applied sciences. 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 in the adoption of state-of-the-art techniques. To accelerate the path from research prototyping to production deployment in imaging science, there is hence a strong need to rethink traditional imaging pipelines, with an emphasis on scalability (for both CPUs and GPUs), high performance computing, and modularity (for customizability). This minisymposium will foster high performance computational imaging by bringing together various research and open-source software communities and showcasing modern production imaging pipelines, both in research and industrial environments.