Super-resolution of turbulent passive scalar images using data assimilation

Abstract

In this paper, the problem of improving the quality of low-resolution passive scalar image sequences is addressed. This situation, known as “image super-resolution” in computer vision, aroused to our knowledge very few applications in the field of fluid visualization. Yet, in most image acquisition devices, the spatial resolution of the acquired data is limited by the sensor physical properties, while users often require higher-resolution images for further processing and analysis of the system of interest. The originality of the approach presented in this paper is to link the image super-resolution process together with the large eddy simulation framework in order to derive a complete super-resolution technique. We first start by defining two categories of fine-scale components we aim to reconstruct. Then, using a deconvolution procedure as well as data assimilation tools, we show how to partially recover some of these missing components within the low-resolution images while ensuring the temporal consistency of the solution. This method is evaluated using both synthetic and real image data. Finally, we demonstrate how the produced high-resolution images can improve a posteriori analysis such as motion field estimation.