  Existing deep learning-based video super-resolution (SR) methods usually
depend on the supervised learning approach, where the training data is usually
generated by the blurring operation with known or predefined kernels (e.g.,
Bicubic kernel) followed by a decimation operation. However, this does not hold
for real applications as the degradation process is complex and cannot be
approximated by these idea cases well. Moreover, obtaining high-resolution (HR)
videos and the corresponding low-resolution (LR) ones in real-world scenarios
is difficult. To overcome these problems, we propose a self-supervised learning
method to solve the blind video SR problem, which simultaneously estimates blur
kernels and HR videos from the LR videos. As directly using LR videos as
supervision usually leads to trivial solutions, we develop a simple and
effective method to generate auxiliary paired data from original LR videos
according to the image formation of video SR, so that the networks can be
better constrained by the generated paired data for both blur kernel estimation
and latent HR video restoration. In addition, we introduce an optical flow
estimation module to exploit the information from adjacent frames for HR video
restoration. Experiments show that our method performs favorably against
state-of-the-art ones on benchmarks and real-world videos.
