计算机应用与软件2025，Vol.42Issue(5)：217-223,297,8.DOI:10.3969/j.issn.1000-386x.2025.05.030
基于注意力与迭代反馈融合的图像超分辨率技术
IMAGE SUPER-RESOLUTION BASED ON ATTENTION AND ITERATIVE FEEDBACK FUSION
摘要
Abstract
Existing deep learning-based image super-resolution networks often lead to redundant computations and a huge amount of parameters,as well as the lack of high-level texture features in the super-resolution results.Aimed at the above problems,an image super-resolution based on attention and iterative feedback fusion network is proposed.The model used a super-resolution architecture of iterative up-and-down sampling.The network used the enhanced attention feedback module to efficiently obtain the corresponding weights of image feature channels by reducing the number of feature channels and enhancing the attention mechanism,ensuring the quality of super-score and reducing the amount of network parameters.In addition,the network model designed a feedback fusion network block,which used the bidirectional iterative feedback fusion of high-level feature information and low-level feature information to maximize information extraction and generate image with higher quality.The experimental results show that compared with the current advanced image super-resolution networks(SRFBN,SMSR,RFAN),the network model has certain advantages in quantitative indicators(PSNR,SSIM)and subjective visual effects.关键词
深度学习/单幅图像超分辨率/迭代上下采样/迭代反馈融合/注意力机制Key words
Deep learning/Single image super-resolution/Iterative up-and-down sampling/Iterative feedback fusion/Attention mechanism分类
信息技术与安全科学引用本文复制引用
朱鉴,蔡金峰,陈炳丰,迟小羽,蔡瑞初..基于注意力与迭代反馈融合的图像超分辨率技术[J].计算机应用与软件,2025,42(5):217-223,297,8.基金项目
国家重点研发计划项目(2021ZD011150) （2021ZD011150）
国家自然科学基金优秀青年基金项目(6212200101) （6212200101）
国家 自然科学基金项目(61976052) （61976052）
中国高等教育学会实验室研究专项项目(21SYYB17) （21SYYB17）
广东省自然科学基金项目(2016A030310342) （2016A030310342）
广东省科技计划项目(2016A040403078,2017B010110015,2017B010110007) （2016A040403078,2017B010110015,2017B010110007）
广州市科技计划项目(201604016075,202007040005). （201604016075,202007040005）
