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Despite convolutional network-based methods have boosted the performance ofsingle image super-resolution (SISR), the huge computation costs restrict theirpractical applicability. In this paper, we develop a computation efficient yetaccurate network based on the proposed attentive auxiliary features (A$^2$F)for SISR. Firstly, to explore the features from the bottom layers, theauxiliary feature from all the previous layers are projected into a commonspace. Then, to better utilize these projected auxiliary features and filterthe redundant information, the channel attention is employed to select the mostimportant common feature based on current layer feature. We incorporate thesetwo modules into a block and implement it with a lightweight network.Experimental results on large-scale dataset demonstrate the effectiveness ofthe 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 allscales, which proves its ability to better utilize the auxiliary features.Codes are available at https://github.com/wh/A2F-SR.

Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning