Lightweight Image Super-Resolution Based on Shuffle Group Convolution and Sparse Global Attention

Convolutional neural networks have been widely used in the field of image superresolution, 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 superresolution 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 Transfomerbased 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. 66×10、130×10, and 930 ms in parameter quantity, amount of calculation, and running time, respectively.