论文标题
带有细心的辅助功能学习的轻量级单像超分辨率网络
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning
论文作者
论文摘要
尽管基于卷积的网络方法提高了单图超分辨率（SISR）的性能，但巨大的计算成本限制了其实际适用性。在本文中，我们根据SISR的建议的辅助功能（$^2 $ f）开发了一个计算有效而准确的网络。首先，为了探索底层的功能，所有先前层的辅助功能都投影到一个公共空间中。然后，为了更好地利用这些投射的辅助功能并过滤冗余信息，采用了基于当前层功能的最重要的常见功能。我们将这两个模块合并到一个块中，并通过轻量级网络实现。大规模数据集的实验结果证明了对最先进（SOTA）SR方法的模型的有效性。值得注意的是，当参数小于320k时，$^2 $ f的表现都胜过所有尺度的SOTA方法，这证明了其更好地利用辅助功能的能力。代码可在https://github.com/wxxxxxxh/a2f-sr上找到。
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.
