High Resolution Interferometric Imaging of Liquid-Solid Interfaces with HOTNNET

Abstract

Background: A variety of imaging methods are available to obtain kinematic data at an interface, with a widely varying range of spatial and temporal resolution. These methods require a trade-off between imaging rate and resolution. Objective: A deep learning framework trained on synchronous profilometry data acquired using two imaging modalities at two different spatial resolutions to enhance spatial resolution while maintaining temporal resolution is desired. Methods: Fizeau interferometry (FIF) and frustrated total internal reflection (FTIR) are used to overcome the resolution-rate trade-off via a deep learning framework. The FTIR imaging data are recorded at high resolution, while the FIF imaging data are recorded with a lesser resolved, larger field of view. We apply a deep learning framework using a multi-layer convolutional neural network to enhance the FIF image resolution. Results: With the deep learning framework, we achieve the high spatial resolution of measurements obtained by FTIR imaging in all three dimensions from the lower resolution FIF data. A high-order overset technique ultimately yields full up-scaled images from the network outputs without losing precision. The accuracy of the super-resolved image is evaluated using test data. Conclusions: This hybrid framework, called HOTNNET, is implemented in its entirety on high-speed imaging profilometry data acquired in the study of droplet impacts on a smooth, solid surface, and is used to recover full, high-resolution images at high rates by unwrapping the phase of the interferometry. This framework can be readily adapted to other paired datasets by retraining the network on the novel data.