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
this https URL

本文提出了一种 Lightweight Bimodal Network (LBNet) 方法，它基于 Symmetric CNN 和 Recursive Transformer 实现了高效的单图像超分辨率 (SISR)，相对于同类算法，该方法在计算成本和内存消耗较低的情况下实现了更明显的性能提升。