  Single-image super-resolution (SISR) has achieved significant breakthroughs
with the development of deep learning. However, these methods are difficult to
be applied in real-world scenarios since they are inevitably accompanied by the
problems of computational and memory costs caused by the complex operations. To
solve this issue, we propose a Lightweight Bimodal Network (LBNet) for SISR.
Specifically, an effective Symmetric CNN is designed for local feature
extraction and coarse image reconstruction. Meanwhile, we propose a Recursive
Transformer to fully learn the long-term dependence of images thus the global
information can be fully used to further refine texture details. Studies show
that the hybrid of CNN and Transformer can build a more efficient model.
Extensive experiments have proved that our LBNet achieves more prominent
performance than other state-of-the-art methods with a relatively low
computational cost and memory consumption. The code is available at
https://github.com/IVIPLab/LBNet.
