论文标题
通过对称CNN和递归变压器，轻巧的双峰网络用于单形图像超分辨率
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer
论文作者
论文摘要
通过深度学习的发展，单像超级分辨率（SISR）取得了重大突破。但是，这些方法很难在实际情况下应用，因为它们不可避免地伴随着复杂操作引起的计算和记忆成本问题。为了解决此问题，我们为SISR提出了一个轻型双峰网络（LBNET）。具体而言，有效的对称CNN设计用于局部特征提取和粗大图像重建。同时，我们提出了一个递归变压器，以充分学习图像的长期依赖性，因此可以完全使用全局信息来进一步完善纹理细节。研究表明，CNN和Transform的混合动力可以建立一个更有效的模型。广泛的实验证明，我们的LBNET比其他最先进的方法具有相对较低的计算成本和记忆消耗。该代码可在https://github.com/iviplab/lbnet上找到。
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.
