  Video super-resolution (VSR) aims to estimate a high-resolution (HR) frame
from a low-resolution (LR) frames. The key challenge for VSR lies in the
effective exploitation of spatial correlation in an intra-frame and temporal
dependency between consecutive frames. However, most of the previous methods
treat different types of the spatial features identically and extract spatial
and temporal features from the separated modules. It leads to lack of obtaining
meaningful information and enhancing the fine details. In VSR, there are three
types of temporal modeling frameworks: 2D convolutional neural networks (CNN),
3D CNN, and recurrent neural networks (RNN). Among them, the RNN-based approach
is suitable for sequential data. Thus the SR performance can be greatly
improved by using the hidden states of adjacent frames. However, at each of
time step in a recurrent structure, the RNN-based previous works utilize the
neighboring features restrictively. Since the range of accessible motion per
time step is narrow, there are still limitations to restore the missing details
for dynamic or large motion. In this paper, we propose a group-based
bi-directional recurrent wavelet neural networks (GBR-WNN) to exploit the
sequential data and spatio-temporal information effectively for VSR. The
proposed group-based bi-directional RNN (GBR) temporal modeling framework is
built on the well-structured process with the group of pictures (GOP). We
propose a temporal wavelet attention (TWA) module, in which attention is
adopted for both spatial and temporal features. Experimental results
demonstrate that the proposed method achieves superior performance compared
with state-of-the-art methods in both of quantitative and qualitative
evaluations.
