大连理工大学学报2025，Vol.65Issue(2)：212-220,9.DOI:10.7511/dllgxb202502012
基于轻量化Transformer的高效图像超分辨率算法研究
Study of efficient image super-resolution algorithm based on lightweight Transformer
摘要
Abstract
Transformer-based algorithms have achieved significant performance breakthroughs in image super-resolution field by the strong ability to capture long-range dependencies in images.However,the heavy computational costs and high GPU video memory consumption limit their practical applications,then an efficient image super-resolution algorithm based on lightweight Transformer(LISRFormer)is proposed.A lightweight Transformer is introduced to this algorithm to capture long-range dependencies while reducing the complexity from existing quadratic to linear.By calculating the cross covariance across channels,a transposed attention map that can be applied to large-sized images is obtained.The layer normalization only affects the query and key branches to preserve essential input features.Moreover,an efficient gated depth-wise-convolution feed-forward network(EGDFN)is designed as the feed-forward network in Transformer to further restore accurate texture information.Numerous quantitative and qualitative experiments conducted on benchmark datasets show that this algorithm outperforms existing lightweight image super-resolution algorithms in terms of computational cost and image reconstruction quality.关键词
图像超分辨率/Transformer/轻量化/注意力Key words
image super-resolution/Transformer/lightweight/attention分类
信息技术与安全科学引用本文复制引用
高翔,王凡,胡小鹏..基于轻量化Transformer的高效图像超分辨率算法研究[J].大连理工大学学报,2025,65(2):212-220,9.基金项目
国家科技重大专项资助项目(2018YFA0704605). （2018YFA0704605）
