Deep learning (DL) architectures for superresolution (SR) normally contain
tremendous parameters, which has been regarded as the crucial advantage for
obtaining satisfying performance. However, with the widespread use of mobile
phones for taking and retouching photos, this character greatly hampers the
deployment of DL-SR models on the mobile devices. To address this problem, in
this paper, we propose a super lightweight SR network: s-LWSR. There are mainly
three contributions in our work. Firstly, in order to efficiently abstract
features from the low resolution image, we build an information pool to mix
multi-level information from the first half part of the pipeline. Accordingly,
the information pool feeds the second half part with the combination of
hierarchical features from the previous layers. Secondly, we employ a
compression module to further decrease the size of parameters. Intensive
analysis confirms its capacity of trade-off between model complexity and
accuracy. Thirdly, by revealing the specific role of activation in deep models,
we remove several activation layers in our SR model to retain more information
for performance improvement. Extensive experiments show that our s-LWSR, with
limited parameters and operations, can achieve similar performance to other
cumbersome DL-SR methods.

通过建立信息池、使用压缩模块和去除激活层，本文提出了一个超轻量级的超分辨率网络 s-LWSR，以减少参数数量和操作次数，在保持性能的同时，在移动设备上部署 DL-SR 模型时具有明显的优势。