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.
The architecture of A$^2$F with 4 attentive auxiliary feature blocks. The architecture of A$^2$F with more attentive auxiliary feature blocks is similar.
Qualitative comparison over datasets for scale $\times4$. The red rectangle indicates the area of interest for zooming. Comparison for other two datasets can be seen supplementary material.
Running time comparison with $\times4$ scale on Urban100 dataset. All of them are evaluated on the same mechine.
Evaluation on five datasets by scale $\times2$, $\times3$, $\times4$. Red and blue imply the best and second best result in a group, respectively.
1 | @InProceedings{Wang_2020_ACCV, |
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