The single image super-resolution(SISR) algorithms under deep learning currently have two main models, one based on convolutional neural networks and the other based on Transformer. The former uses the stacking of convolutional layers with different convolutional kernel sizes to design the model, which enables the model to better extract the local features of the image; the latter uses the self-attention mechanism to design the model, which allows the model to establish long-distance dependencies between image pixel points through the self-attention mechanism and then better extract the global features of the image. However, both of the above methods face their problems. Based on this, this paper proposes a new lightweight multi-scale feature fusion network model based on two-way complementary convolutional and Transformer, which integrates the respective features of Transformer and convolutional neural networks through a two-branch network architecture, to realize the mutual fusion of global and local information. Meanwhile, considering the partial loss of information caused by the low-pixel images trained by the deep neural network, this paper designs a modular connection method of multi-stage feature supplementation to fuse the feature maps extracted from the shallow stage of the model with those extracted from the deep stage of the model, to minimize the loss of the information in the feature images that is beneficial to the image restoration as much as possible, to facilitate the obtaining of a higher-quality restored image. The practical results finally show that the model proposed in this paper is optimal in image recovery performance when compared with other lightweight models with the same amount of parameters.

翻译：当前基于深度学习的单图像超分辨率（SISR）算法主要存在两种模型，一种基于卷积神经网络，另一种基于Transformer。前者通过堆叠不同卷积核大小的卷积层来设计模型，使模型能够更好地提取图像的局部特征；后者利用自注意力机制设计模型，使模型能够通过自注意力机制建立图像像素点之间的长距离依赖关系，从而更好地提取图像的全局特征。然而，上述两种方法均面临各自的问题。基于此，本文提出了一种新的基于双向互补卷积与Transformer的轻量级多尺度特征融合网络模型，该模型通过双分支网络架构，融合了Transformer与卷积神经网络各自的特性，实现了全局信息与局部信息的相互融合。同时，考虑到深度神经网络训练低像素图像时导致的信息部分丢失问题，本文设计了一种多阶段特征补充的模块化连接方式，将模型浅层阶段提取的特征图与深层阶段提取的特征图进行融合，以尽可能减少对图像复原有益的特征信息损失，从而有助于获得更高质量的复原图像。最终实验结果表明，与参数量相当的其他轻量级模型相比，本文提出的模型在图像复原性能上达到最优。