  The extraction and proper utilization of convolution neural network (CNN)
features have a significant impact on the performance of image super-resolution
(SR). Although CNN features contain both the spatial and channel information,
current deep techniques on SR often suffer to maximize performance due to using
either the spatial or channel information. Moreover, they integrate such
information within a deep or wide network rather than exploiting all the
available features, eventually resulting in high computational complexity. To
address these issues, we present a binarized feature fusion (BFF) structure
that utilizes the extracted features from residual groups (RG) in an effective
way. Each residual group (RG) consists of multiple hybrid residual attention
blocks (HRAB) that effectively integrates the multiscale feature extraction
module and channel attention mechanism in a single block. Furthermore, we use
dilated convolutions with different dilation factors to extract multiscale
features. We also propose to adopt global, short and long skip connections and
residual groups (RG) structure to ease the flow of information without losing
important features details. In the paper, we call this overall network
architecture as hybrid residual attention network (HRAN). In the experiment, we
have observed the efficacy of our method against the state-of-the-art methods
for both the quantitative and qualitative comparisons.
