Multilevel attention residual network for super-resolution reconstruction

In our article, U-Net network is selected as the basic network architecture, and a super-resolution reconstruction algorithm of multi-level image feature fusion is designed. Among them, the network architecture of Residual-in-Residual Dense Block (RRDB) is introduced, and light-weight attention with channel attention mechanism (CA) and spatial attention (SA) mechanism is added to the RRDB residual block. On this basis, the multi-level attention residual block R are designed through the nesting of residual blocks, and the high-frequency residual features of different levels are extracted through the dense connection of the multi-level residual network, so that the generated super Resolution reconstructed images are richer in texture detail. The network model is trained on the DIV2K and Flickr2K training sets and fine-tuned. Finally, the proposed reconstruction algorithm and comparison algorithms are tested on five commonly used test sets. The experimental results demonstrate that, compared with the comparative algorithm, the improved reconstruction algorithm performs better in terms of objective evaluation metrics PSNR and SSIM, generating super-resolution images that are closer to real high-definition images and more in line with human visual perception.