  Interlacing is a widely used technique, for television broadcast and video
recording, to double the perceived frame rate without increasing the bandwidth.
But it presents annoying visual artifacts, such as flickering and silhouette
"serration," during the playback. Existing state-of-the-art deinterlacing
methods either ignore the temporal information to provide real-time performance
but lower visual quality, or estimate the motion for better deinterlacing but
with a trade-off of higher computational cost. In this paper, we present the
first and novel deep convolutional neural networks (DCNNs) based method to
deinterlace with high visual quality and real-time performance. Unlike existing
models for super-resolution problems which relies on the translation-invariant
assumption, our proposed DCNN model utilizes the temporal information from both
the odd and even half frames to reconstruct only the missing scanlines, and
retains the given odd and even scanlines for producing the full deinterlaced
frames. By further introducing a layer-sharable architecture, our system can
achieve real-time performance on a single GPU. Experiments shows that our
method outperforms all existing methods, in terms of reconstruction accuracy
and computational performance.
