Image Super-Resolution Reconstruction Based on Attention Mechanism of Feature Map

Abstract

High-frequency components in image Super-Resolution(SR) reconstruction usually include more details such as contour and texture.In order to deal with the high-frequency components and low-frequency components in feature map better and adjust the channel features adaptively,this paper proposes an image SR reconstruction network model based on the attention mechanism.The model uses the feature extraction module to extract the feature information from the original Low-Resolution(LR) image.Then multiple information extraction modules using the attention mechanism of the feature map are used to adjust the channel features adaptively through the interdependence between the feature channels,so as to recover more detailed information.On this basis,the reconstruction module is used to reconstruct High-Resolution(HR) images of different scales.The experimental results on the Set5 dataset show that compared with the reconstruction model based on Bicubic interpolation,this model can effectively improve the visual effect of the image,and its Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity(SSIM) are improved by 3.92 dB and 0.056 respectively.