电讯技术2026，Vol.66Issue(1)：55-64,10.DOI:10.20079/j.issn.1001-893x.240903005
局部-全局特征增强的Transformer合成孔径光学图像复原方法
Image Restoration Method for Transformer Synthetic Aperture Optical System with Local-Global Feature Enhancement
摘要
Abstract
In the synthetic aperture optical imaging system,due to insufficient light transmission area and translation errors,the final imaging is prone to exhibit degraded blurring.Traditional restoration methods based on mathematical models are difficult to be applied across different systems.A network based on local-global feature-enhancing Transformer is proposed to solve the problem of difficult restoration of high-resolution degraded images in synthetic aperture optical systems.A residual convolution layer based on gated mechanism is proposed,which utilizes deformable convolution and simple gated mechanism to focus on the local features of the image.A Transformer layer based on linear attention and gated mechanism is constructed to reduce the computational complexity while establishing long-distance dependency relationships between image information.For the ringing phenomenon generated in synthetic aperture optical imaging systems,an adaptive scale feature enhancement module is proposed.For features at different scales,secondary learning is conducted using feature weights,enhancing the sharpness expression ability of structural information in the features and avoiding interference from artifacts generated by the ringing phenomenon during the restoration process.Experimental results show that this network reduces the computational complexity by 37.85%while the peak signal-to-noise ratio and structural similarity are on average improved by 8.07%and 3.17%compared with those of other methods,and can effectively restore high-resolution degraded images in synthetic aperture optical systems.关键词
合成孔径光学图像/图像复原/局部-全局特征增强Key words
synthetic aperture optical image/image restoration/local-global feature enhancement分类
信息技术与安全科学引用本文复制引用
童俊毅,张银胜..局部-全局特征增强的Transformer合成孔径光学图像复原方法[J].电讯技术,2026,66(1):55-64,10.基金项目
国家自然科学基金资助项目(62071240,62106111) （62071240,62106111）
2024年江苏省研究生创新项目(SJCX24_0446) （SJCX24_0446）
无锡市科技发展资金"太湖之光"科技攻关项目(K20231004) （K20231004）
