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.