  The convolutional neural network model for optical flow estimation usually
outputs a low-resolution(LR) optical flow field. To obtain the corresponding
full image resolution,interpolation and variational approach are the most
common options, which do not effectively improve the results. With the
motivation of various convolutional neural network(CNN) structures succeeded in
single image super-resolution(SISR) task, an end-to-end convolutional neural
network is proposed to reconstruct the high resolution(HR) optical flow field
from initial LR optical flow with the guidence of the first frame used in
optical flow estimation. Our optical flow super-resolution(OFSR) problem
differs from the general SISR problem in two main aspects. Firstly, the optical
flow includes less texture information than image so that the SISR CNN
structures can't be directly used in our OFSR problem. Secondly, the initial LR
optical flow data contains estimation error, while the LR image data for SISR
is generally a bicubic downsampled, blurred, and noisy version of HR ground
truth. We evaluate the proposed approach on two different optical flow
estimation mehods and show that it can not only obtain the full image
resolution, but generate more accurate optical flow field (Accuracy improve 15%
on FlyingChairs, 13% on MPI Sintel) with sharper edges than the estimation
result of original method.
