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

Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer