卷积神经网络已在图像超分辨率领域得到广泛应用，Transformer近年来在该类图像处理任务中的扩展更是具有里程碑的意义，然而这些大型网络具有过多的参数量和计算量，其在部署和应用上存在很大局限性。考虑到上述发展现状，提出一种基于交错组卷积与稀疏全局注意力的轻量级图像超分辨率重建网络，该网络引入了以交错组卷积为主的特征提取模块，对Transformer的多头自注意力机制进行优化，设计了一种稀疏全局注意力机制以增强特征学习能力，并提出了一种多尺度特征重构模块来提高重建效果。实验结果表明：相比其他几种基于深度神经网络的方法，所提方法的PSNR、SSIM、参数量、计算量等性能指标都表现较好。而与基于Transfomer的方法相比，所提方法在PSNR、SSIM指标上平均提高0.03、0.0002，在参数量、计算量、运行时间上平均降低2.66×10⁶、130×10⁹、930 ms。

Convolutional neural networks have been widely used in the field of image super-resolution, and the expansion of the transformer in such image processing tasks is a milestone in recent years. However, these large networks have excessive parameters and entail a large amount of computation, limiting their deployment and application. Given the above development status, a network based on staggered group convolution and sparse global attention lightweight image super-resolution reconstruction is proposed. A staggered group convolution feature extraction module is introduced in the network and in the transformer to improve attention mechanism optimization, and a sparse global attention mechanism is designed to enhance the feature learning ability. A multiscale feature reconstruction module is put forward to improve the reconstruction effect. The experiments show that compared with several other methods based on deep neural networks,the proposed method performs better in the peak signal to noise ratio (PSNR), structural index similarity (SSIM), parameter quantity, amount of calculation, and other performance indicators. Compared with the Transfomer-based method, the proposed method has an average increase of 0.03 and 0.0002 in PSNR and SSIM, respectively, and an average decrease of 2.66×10⁶、130×10⁹, and 930 ms in parameter quantity, amount of calculation, and running time, respectively.