Image Super-Resolution Reconstruction Based on Dense Residual Attention and Multi-Scale Feature Fusion

Abstract

In order to obtain super-resolution images with richer details and clearer textures, a method for image super-resolution reconstruction using dense residual attention and multi-scale fusion is proposed. First, different scale convolutions are used to fully extract shallow features of the image; then high-frequency features of the image are extracted through one three-layer cascaded multi-scale feature fusion and dense residual attention module, and the reuse of feature map is achieved; finally, residual branches are used to introduce shallow features and high-frequency features of each channel image, and the high-resolution images are reconstructed through up-sampling and sub-pixel convolution. The test results on the Set5, Set14, Bsd100, and Urban100 datasets show that the PSNR and SSIM of our model are superior to most current algorithms, especially in the case of [Formula: see text] reconstruction results. PSNR has improved by 0.2 dB on the Set5 and Bsd100 datasets, and the algorithm has a better subjective visual effect.