  Recent single-image super-resolution (SISR) networks, which can adapt their
network parameters to specific input images, have shown promising results by
exploiting the information available within the input data as well as large
external datasets. However, the extension of these self-supervised SISR
approaches to video handling has yet to be studied. Thus, we present a new
learning algorithm that allows conventional video super-resolution (VSR)
networks to adapt their parameters to test video frames without using the
ground-truth datasets. By utilizing many self-similar patches across space and
time, we improve the performance of fully pre-trained VSR networks and produce
temporally consistent video frames. Moreover, we present a test-time knowledge
distillation technique that accelerates the adaptation speed with less hardware
resources. In our experiments, we demonstrate that our novel learning algorithm
can fine-tune state-of-the-art VSR networks and substantially elevate
performance on numerous benchmark datasets.
