Recently, many works have designed wider and deeper networks to achieve higher image super-resolution performance. Despite their outstanding performance, they still suffer from high computational resources, preventing them from directly applying to embedded devices. To reduce the computation resources and maintain performance, we propose a novel Ghost Residual Attention Network (GRAN) for efficient super-resolution. This paper introduces Ghost Residual Attention Block (GRAB) groups to overcome the drawbacks of the standard convolutional operation, i.e., redundancy of the intermediate feature. GRAB consists of the Ghost Module and Channel and Spatial Attention Module (CSAM) to alleviate the generation of redundant features. Specifically, Ghost Module can reveal information underlying intrinsic features by employing linear operations to replace the standard convolutions. Reducing redundant features by the Ghost Module, our model decreases memory and computing resource requirements in the network. The CSAM pays more comprehensive attention to where and what the feature extraction is, which is critical to recovering the image details. Experiments conducted on the benchmark datasets demonstrate the superior performance of our method in both qualitative and quantitative. Compared to the baseline models, we achieve higher performance with lower computational resources, whose parameters and FLOPs have decreased by more than ten times.

翻译：近年来，许多工作设计了更宽更深的网络以实现更高的图像超分辨率性能。尽管这些网络表现出色，但仍面临高计算资源的限制，难以直接应用于嵌入式设备。为降低计算资源并保持性能，我们提出了一种新型高效超分辨率网络——幽灵残差注意力网络（GRAN）。本文引入幽灵残差注意力块（GRAB）组来克服标准卷积操作的缺陷（即中间特征的冗余性）。GRAB由幽灵模块与通道空间注意力模块（CSAM）组成，以缓解冗余特征的生成。具体而言，幽灵模块通过采用线性运算代替标准卷积来揭示内在特征中的隐藏信息。通过幽灵模块减少冗余特征，我们的模型降低了网络中的内存与计算资源需求。CSAM则更全面地关注特征提取的位置与内容，这对恢复图像细节至关重要。在基准数据集上的实验表明，我们的方法在定性与定量两方面均具有优越性能。与基线模型相比，我们以更低的计算资源实现了更高性能，其参数量和浮点运算次数均减少了十倍以上。