I have a series of rainfall intensity images from a weather radar taken every 10 minutes. My goal is to generate intermediate frames in order to create a slow motion video. I've tried using the Farnebeck motion interpolation / optical flow algorithm implemented in OpenCV but the results were... not too bad but definitely not satisfactory.
What are some other ways to approach this problem? I was thinking that maybe something vaguely similar to Hidden Markov Models could work because the problem can be modelled as a Markov chain, where each state consists of an observed part (rainfall intensity image) and an unobserved part (wind speed and direction, etc.). The intermediate frames would be fully unobserved.
Or, I've seen impressive results of motion interpolation via deep neural networks, but they were trained on high FPS videos. I don't have the intermediate frames available for training the neural network.