  Single image super-resolution (SISR) is one of the most challenging problems
in the field of computer vision. Among the deep convolutional neural network
based methods, attention mechanism has shown the enormous potential. However,
due to the diverse network architectures, there is a lack of a universal
attention mechanism for the SISR task. In this paper, we propose a lightweight
and efficient Balanced Attention Mechanism (BAM), which can be generally
applicable for different SISR networks. It consists of Avgpool Channel
Attention Module (ACAM) and Maxpool Spatial Attention Module (MSAM). These two
modules are connected in parallel to minimize the error accumulation and the
crosstalk. To reduce the undesirable effect of redundant information on the
attention generation, we only apply Avgpool for channel attention because
Maxpool could pick up the illusive extreme points in the feature map across the
spatial dimensions, and we only apply Maxpool for spatial attention because the
useful features along the channel dimension usually exist in the form of
maximum values for SISR task. To verify the efficiency and robustness of BAM,
we apply it to 12 state-of-the-art SISR networks, among which eight were
without attention thus we plug BAM in and four were with attention thus we
replace its original attention module with BAM. We experiment on Set5, Set14
and BSD100 benchmark datasets with the scale factor of x2 , x3 and x4 . The
results demonstrate that BAM can generally improve the network performance.
Moreover, we conduct the ablation experiments to prove the minimalism of BAM.
Our results show that the parallel structure of BAM can better balance channel
and spatial attentions, thus outperforming the series structure of prior
Convolutional Block Attention Module (CBAM).
