Hu Lei, Hu Long, Chen MingHui
School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China.
Sci Rep. 2024 Jul 6;14(1):15585. doi: 10.1038/s41598-024-66302-8.
Infrared images have important applications in military, security and surveillance fields. However, limited by technical factors, the resolution of infrared images is generally low, which seriously limits the application and development of infrared images in various fields. To address the problem of difficult recovery of edge information and easy ringing effect in the super-resolution reconstruction process of infrared images, an edge-enhanced infrared image super-resolution reconstruction model TESR under transformer is proposed. The main structure of this model is transformer. First, in view of the problem of difficult recovery of edge information of infrared images, an edge detection auxiliary network is designed, which can obtain more accurate edge information from the input low-resolution images and enhance the edge details during image reconstruction; then, the CSWin Transformer is introduced to compute the self-attention of horizontal and vertical stripes in parallel, so as to increase the receptive field of the model and enable it to utilize features with higher semantic levels. The super-resolution reconstruction model proposed in this paper can extract more comprehensive image information, and at the same time, it can obtain more accurate edge information to enhance the texture details of super-resolution images, and achieve better reconstruction results.
红外图像在军事、安全和监视领域有着重要应用。然而，受技术因素限制，红外图像的分辨率普遍较低，这严重制约了红外图像在各领域的应用与发展。为解决红外图像超分辨率重建过程中边缘信息恢复困难和易出现振铃效应的问题，提出了一种基于Transformer的边缘增强红外图像超分辨率重建模型TESR。该模型的主要结构是Transformer。首先，针对红外图像边缘信息恢复困难的问题，设计了一个边缘检测辅助网络，它能从输入的低分辨率图像中获取更准确的边缘信息，并在图像重建过程中增强边缘细节；然后，引入CSWin Transformer并行计算水平和垂直条纹的自注意力，以增加模型的感受野，使其能够利用更高语义层次的特征。本文提出的超分辨率重建模型能够提取更全面的图像信息，同时，能够获得更准确的边缘信息以增强超分辨率图像的纹理细节，取得更好的重建效果。
