  The popularity of high and ultra-high definition displays has led to the need
for methods to improve the quality of videos already obtained at much lower
resolutions. Current Video Super-Resolution methods are not robust to mismatch
between training and testing degradation models since they are trained against
a single degradation model (usually bicubic downsampling). This causes their
performance to deteriorate in real-life applications. At the same time, the use
of only the Mean Squared Error during learning causes the resulting images to
be too smooth. In this work we propose a new Convolutional Neural Network for
video super resolution which is robust to multiple degradation models. During
training, which is performed on a large dataset of scenes with slow and fast
motions, it uses the pseudo-inverse image formation model as part of the
network architecture in conjunction with perceptual losses, in addition to a
smoothness constraint that eliminates the artifacts originating from these
perceptual losses. The experimental validation shows that our approach
outperforms current state-of-the-art methods and is robust to multiple
degradations.
