GANs-based PIV resolution enhancement without the need of high-resolution input


  • Alejandro Güemes Universidad Carlos III de Madrid, Spain
  • Carlos Sanmiguel Vila Universidad Carlos III de Madrid, Spain
  • Stefano Discetti Universidad Carlos III de Madrid, Spain



deep learning, super-resolution, GANs, PTV


A data-driven approach to reconstruct high-resolution flow fields is presented. The method is based on exploiting the recent advances of SRGANs (Super-Resolution Generative Adversarial Networks) to enhance the resolution of Particle Image Velocimetry (PIV). The proposed approach exploits the availability of incomplete projections on high-resolution fields using the same set of images processed by standard PIV. Such incomplete projection is made available by sparse particle-based measurements such as super-resolution particle tracking velocimetry. Consequently, in contrast to other works, the method does not need a dual set of low/high-resolution images, and can be applied directly on a single set of raw images for training and estimation. This data-enhanced particle approach is assessed employing two datasets generated from direct numerical simulations: a fluidic pinball and a turbulent channel flow. The results prove that this data-driven method is able to enhance the resolution of PIV measurements even in complex flows without the need of a separate high-resolution experiment for training.

Author Biography

Stefano Discetti, Universidad Carlos III de Madrid, Spain

Stefano Discetti is Associate Professor and Director of the Aerospace Engineering Research Group at Universidad Carlos III de Madrid. He received his Ph.D. (2013) in Aerospace Engineering from University of Naples Federico II. His main research interests are in the field of development of optical measurement techniques for fluid flows, data-driven techniques and machine learning applied to flow measurements and flow control. He is PI of the ERC Starting Grant project NEXTFLOW. He is member of the Editorial Board of the JCR journal Measurement Science and Technology, of the Scientific Committee of the International Symposia on Particle Image Velocimetry, of the Scientific Council of the International Centre for Heat and Mass Transfer and of the Steering Committee of the Special Interest Group on Particle Image Velocimetry of ERCOFTAC.






Deep Learning and Data Assimilation