Open AccessGANs-based PIV resolution enhancement without the need of high-resolution input
Author(s) -
Alejandro Güemes,
Carlos Sanmiguel Vila,
Stefano Discetti
Publication year - 2021
Publication title -
international symposium on particle image velocimetry.
Language(s) - English
Resource type - Journals
ISSN - 2769-7576
DOI - 10.18409/ispiv.v1i1.160
Subject(s) - resolution (logic) , computer science , particle image velocimetry , data set , artificial intelligence , image resolution , set (abstract data type) , projection (relational algebra) , computer vision , tracking (education) , channel (broadcasting) , algorithm , turbulence , physics , psychology , computer network , pedagogy , thermodynamics , programming language
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.