  The video super-resolution (VSR) method based on the recurrent convolutional
network has strong temporal modeling capability for video sequences. However,
the temporal receptive field of different recurrent units in the unidirectional
recurrent network is unbalanced. Earlier reconstruction frames receive less
spatio-temporal information, resulting in fuzziness or artifacts. Although the
bidirectional recurrent network can alleviate this problem, it requires more
memory space and fails to perform many tasks with low latency requirements. To
solve the above problems, we propose an end-to-end information prebuilt
recurrent reconstruction network (IPRRN), consisting of an information prebuilt
network (IPNet) and a recurrent reconstruction network (RRNet). By integrating
sufficient information from the front of the video to build the hidden state
needed for the initially recurrent unit to help restore the earlier frames, the
information prebuilt network balances the input information difference at
different time steps. In addition, we demonstrate an efficient recurrent
reconstruction network, which outperforms the existing unidirectional recurrent
schemes in all aspects. Many experiments have verified the effectiveness of the
network we propose, which can effectively achieve better quantitative and
qualitative evaluation performance compared to the existing state-of-the-art
methods.
