哈尔滨商业大学学报（自然科学版）2025，Vol.41Issue(2)：143-151,9.
基于双域增强Transformer的图像超分辨率重建
Image super-resolution reconstruction based on double-domain enhanced Transformer
摘要
Abstract
To address the limitation of existing image super-resolution methods in effectively capturing fine image details,which leads to the generation of low-quality images,a double-domain enhanced Transformer(DDET)was proposed for image super-resolution reconstruction.The algorithm designed the model from two perspectives:learning spatial and frequency domain information.By alternately connecting the spatial-domain enhanced Transformer block(SETB)and the frequency-domain enhanced Transformer block(FETB),The spatial domain information was extracted while the frequency domain information was effectively learned,which further enhances the network information extraction capability.In addition,To make the network pay more attention to global and local information,a special convolution structure was designed to be integrated with the frequency domain information extraction module to improve the quality of reconstructed images further.Compared with multi-scale residual network for image super-resolution(MSRN),the peak signal-to-noise ratio(RPSN)improved by0.37 dB,0.22 dB,0.69 dB,and0.18 dB on standard test sets,including Set5,Set14,Urban100,and BSD100,at the magnification of 3.Visual effects show that DDET generates clearer image textures.Experimental results demonstrated that DDET effectively captures finer details,produce higher-quality images,and achieved superior overall performance.关键词
图像超分辨率/Transformer/频域/傅里叶变换/深度学习/注意力机制Key words
image super-resolution/transformer/frequency domain/fourier transform/deep learning/attention mechanism分类
计算机与自动化引用本文复制引用
傅慧滢,杨高明,王瑜..基于双域增强Transformer的图像超分辨率重建[J].哈尔滨商业大学学报（自然科学版）,2025,41(2):143-151,9.基金项目
国家自然科学基金资助项目(52374155) （52374155）
安徽省自然科学基金资助项目(2308085MF218). （2308085MF218）
