Image super-resolution reconstruction of multi-scale residual dense attention network
NI Shuiping
WANG Shijie
LI Huifang
LI Pengkun
Abstract：Objective Using a single-scale convolutional network to extract low-resolution(LR)image fea-tures could cause a large number of image high-frequency features to be lost.In order to obtain more high-frequency features and reconstruct clearer super-resolution images,Methods a single image super-resolution reconstruction algorithm based on multi-scale residual dense attention network was proposed.Firstly,the convolutional network was used to extract shallow features from low-resolution images and the shallow features were used as input at all levels of the subsequent network.Secondly,the multi-scale re-sidual dense attention blocks at all levels were used to process the image features of the previous network and extracted the high-frequency features of the image.The multi-scale residual dense network was good at ex-tracting richer image features and attention mechanism was fused into the network to make high-frequency region features get more attention.Then,the image features of different depths were extracted at all levels of the network for global feature fusion.Finally,the fused features were up-sampled to output the reconstructed super-resolution image.Results When the upscale factor was set as 4,the network was tested on the SET5,SET14,BSDS100,URBAN100 and MANGA109 datasets,and the peak signal-to-noise ratios were 31.97,28.58,27.57,25.85 and 29.79 dB,respectively.The basic modules in the network were replaced by multi-scale residual dense attention blocks,residual blocks and dense blocks to extract features.The peak signal-to-noise ratio was used as the module performance evaluation standard,and the multi-scale residual dense attention block performed better.Conclusion The network combined with the multi-scale residual dense net-work could obtain richer high and low frequency information of the image.The attention mechanism was fused to effectively extract the high frequency information in the network,and the super-resolution image with clearer texture could be reconstructed.
Keywords：multi-scale residualdense attention networksuper-resolution reconstructionattention mechanismhigh frequency region
Publication Date：2024-02-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 140-148 )