Multi-scale hybrid attention network for image super-resolution reconstruction

Abstract

Aiming at the problems of high-frequency detail loss and structural distortion in images reconstructed by existing super-resolution (SR) reconstruction algorithms, a new reconstruction algorithm is proposed by combining multi-scale hybrid attention network. Firstly, a multi-scale residual module (MRM) was designed to extract features of different scale information and fuse them to obtain shallow features containing more information. Secondly, the residual hybrid attention module (RHAM) was used to enhance the network feature extraction ability along two different dimensions of channel and space, and adaptive feature optimization was carried out to improve the reuse of high-frequency features. Finally, the extracted features were enhanced through the reconstruction module to obtain corresponding high-resolution images. Tested on a benchmark data set, the experimental results show that the proposed algorithm outperforms mainstream image SR algorithms by an average of 0.104, 0.224, 0.146 dB in peak signal to noise ratio (PSNR), and 0.034 9, 0.027 6, and 0.018 1 in structural similarity index measure (SSIM) at magnifications of two, three, and four times. This algorithm can more effectively utilize the original image information, and the reconstructed image has certain improvements in edge and texture details.