红外技术2026，Vol.48Issue(1)：52-61,10.
基于改进Transformer网络的红外图像去模糊技术
Infrared Image Deblurring Using Enhanced Transformer Networks
摘要
Abstract
To address the degradation of infrared images caused by translation motion,radial motion,and turbulence blurs,a transformer-based infrared image deblurring neural network using deformable sampling and multidirectional shifting windows(DMST)is proposed.With deformable sampling and a multi-direction window shifting mechanism,the proposed method allows self-attention windows to better conform to the irregular distribution of informative features in images while enhancing the network's perception of interactions with global features.On three infrared datasets featuring translation motion,radial motion,and turbulence blurs,the proposed method achieves peak signal-to-noise ratios(PSNR)of 35.69,30.19,and 33.55 dB,respectively,and structure similarity index measure(SSIM)values of 0.9566,0.8882,and 0.9230,respectively.The experimental results indicate that compared to the advanced deblurring methods reported in recent years,the proposed method exhibits superior deblurring performance for infrared images.关键词
红外成像/图像处理/图像去模糊/神经网络/注意力机制Key words
infrared imaging/image processing/image deblurring/neural networks/attention mechanism分类
信息技术与安全科学引用本文复制引用
张辰,张皓然,张琢,洪闻青,王晓东..基于改进Transformer网络的红外图像去模糊技术[J].红外技术,2026,48(1):52-61,10.基金项目
基础加强计划技术领域基金(2022-JCJQ-JJ-0206). （2022-JCJQ-JJ-0206）
