Deep learning-based approaches have demonstrated impressive performance in single-image super-resolution (SISR). Efficient super-resolution compromises the reconstructed image’s quality to have fewer parameters and Flops. Ensured efficiency in image reconstruction and improved reconstruction quality of the model are significant challenges. This paper proposes a trio branch module (TBM) based on structural reparameterization. TBM achieves equivalence transformation through structural reparameterization operations, which use a complex network structure in the training phase and convert it to a more lightweight structure in the inference, achieving efficient inference while maintaining accuracy. Based on the TBM, we further design a lightweight version of the enhanced spatial attention mini (ESA-mini) and the residual trio feature block (RTFB). Moreover, the multiple RTFBs are combined to construct the residual trio network (RTFN). Finally, we introduce a localized contrast loss for better applicability to the super-resolution task, which enhances the reconstruction quality of the super-resolution model. Experiments show that the RTFN framework proposed in this paper outperforms other state-of-the-art efficient super-resolution methods in terms of inference speed and reconstruction quality.

基于深度学习的方法在单图像超分辨率 （SISR） 方面表现出令人印象深刻的性能。高效的超分辨率会牺牲重建图像的质量，以获得更少的参数和 Flops。确保图像重建效率和提高模型的重建质量是重大挑战。该文提出了一种基于结构重参数化的三元分支模块（TBM）。TBM 通过结构再参数化操作实现等价转换，在训练阶段使用复杂的网络结构，并在推理中将其转换为更轻量级的结构，在保持准确性的同时实现高效推理。在 TBM 的基础上，我们进一步设计了增强空间注意力迷你 （ESA-mini） 和残余三重功能块 （RTFB） 的轻量级版本。此外，将多个 RTFBs 组合以构建残差三重网络 （RTFN）。最后，我们引入了局部对比度损失，以更好地适用于超分辨率任务，从而提高了超分辨率模型的重建质量。实验表明，本文提出的 RTFN 框架在推理速度和重建质量方面优于其他最先进的高效超分辨率方法。