  Recovering texture information from the aliasing regions has always been a
major challenge for Single Image Super Resolution (SISR) task. These regions
are often submerged in noise so that we have to restore texture details while
suppressing noise. To address this issue, we propose a Balanced Attention
Mechanism (BAM), which consists of Avgpool Channel Attention Module (ACAM) and
Maxpool Spatial Attention Module (MSAM) in parallel. ACAM is designed to
suppress extreme noise in the large scale feature maps while MSAM preserves
high-frequency texture details. Thanks to the parallel structure, these two
modules not only conduct self-optimization, but also mutual optimization to
obtain the balance of noise reduction and high-frequency texture restoration
during the back propagation process, and the parallel structure makes the
inference faster. To verify the effectiveness and robustness of BAM, we applied
it to 10 SOTA SISR networks. The results demonstrate that BAM can efficiently
improve the networks performance, and for those originally with attention
mechanism, the substitution with BAM further reduces the amount of parameters
and increases the inference speed. Moreover, we present a dataset with rich
texture aliasing regions in real scenes, named realSR7. Experiments prove that
BAM achieves better super-resolution results on the aliasing area.
