  Super-resolution is an innovative technique that upscales the resolution of
an image or a video and thus enables us to reconstruct high-fidelity images
from low-resolution data. This study performs super-resolution analysis on
turbulent flow fields spatially and temporally using various state-of-the-art
machine learning techniques like ESPCN, ESRGAN and TecoGAN to reconstruct
high-resolution flow fields from low-resolution flow field data, especially
keeping in mind the need for low resource consumption and rapid results
production/verification. The dataset used for this study is extracted from the
'isotropic 1024 coarse' dataset which is a part of Johns Hopkins Turbulence
Databases (JHTDB). We have utilized pre-trained models and fine tuned them to
our needs, so as to minimize the computational resources and the time required
for the implementation of the super-resolution models. The advantages presented
by this method far exceed the expectations and the outcomes of regular single
structure models. The results obtained through these models are then compared
using MSE, PSNR, SAM, VIF and SCC metrics in order to evaluate the upscaled
results, find the balance between computational power and output quality, and
then identify the most accurate and efficient model for spatial and temporal
super-resolution of turbulent flow fields.
