  Despite convolutional network-based methods have boosted the performance of
single image super-resolution (SISR), the huge computation costs restrict their
practical applicability. In this paper, we develop a computation efficient yet
accurate network based on the proposed attentive auxiliary features (A$^2$F)
for SISR. Firstly, to explore the features from the bottom layers, the
auxiliary feature from all the previous layers are projected into a common
space. Then, to better utilize these projected auxiliary features and filter
the redundant information, the channel attention is employed to select the most
important common feature based on current layer feature. We incorporate these
two modules into a block and implement it with a lightweight network.
Experimental results on large-scale dataset demonstrate the effectiveness of
the proposed model against the state-of-the-art (SOTA) SR methods. Notably,
when parameters are less than 320k, A$^2$F outperforms SOTA methods for all
scales, which proves its ability to better utilize the auxiliary features.
Codes are available at https://github.com/wxxxxxxh/A2F-SR.
