摘要
基于深度学习的图像超分辨率(SR)重建方法主要通过增加模型的深度来提升图像重建的质量,但同时增加了模型的计算代价,很多网络利用注意力机制来提高特征提取能力,但难以充分学习到不同区域的特征。为此,提出一种基于期望最大化(EM)自注意力残差的图像超分辨率重建网络。该网络通过改进基础残差块,构建特征增强残差块,以更好地复用残差块中所提取的特征。为增加特征信息在空间上的相关性,引入EM自注意力机制,构建EM自注意力残差模块来增强模型中每个模块的特征提取能力,并通过级联EM自注意力残差模块来构建整个模型的特征提取结构。所获得的特征图通过上采样的图像重建模块获得重建的高分辨率图像。将所提方法与主流方法进行实验对比,结果表明:所提方法在5个流行的SR测试集上能够取得较好的主观视觉效果和更优的性能指标。
In recent years,most deep learning-based image super-resolution(SR)reconstruction methods mainly improve the quality of image reconstruction by increasing the depth of the model,while also increasing the computational cost of the model.Additionally,a lot of networks have implemented the attention mechanism to enhance their capacity for feature extraction,but it is still challenging to properly understand the properties of various regions.In response to the above problems,this paper proposes a novel SR reconstruction network based on expectation maximization(EM)self-attention residual.The network constructs a feature-enhanced residual block by improving the basic residual block to better reuse the features extracted from the residual block.In order to increase the spatial correlation of the feature information,an EM self-attention residual block is constructed by introducing the EM self-attention mechanism,which is used to enhance the feature extraction capability of each module in the deep network model.Moreover,the feature extraction structure of the entire model is constructed by cascading EM self-attention residual blocks.Finally,a reconstructed high-resolution image is obtained through an up-sampling image reconstruction module.In order to verify the effectiveness of the proposed method,this paper has carried out comparison experiments with some mainstream methods.The experimental results show that the proposed method can achieve better subjective visual effects and better objective evaluation indicators on five popular widely used SR test datasets.
作者
黄淑英
胡瀚洋
杨勇
万伟国
吴峥
HUANG Shuying;HU Hanyang;YANG Yong;WAN Weiguo;WU Zheng(School of Software,Tiangong University,Tianjin 300387,China;School of Information Management,Jiangxi University of Finance and Economics,Nanchang 330032,China;School of Computer Science and Technology,Tiangong University,Tianjin 300387,China;School of Software and Internet of Things Engineering,Jiangxi University of Finance and Economics,Nanchang 330032,China)
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2024年第2期388-397,共10页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家自然科学基金(61862030,62072218)
江西省自然科学基金(20192ACB20002,20192ACBL21008)*。
关键词
超分辨率重建
注意力机制
期望最大化
特征增强残差块
EM自注意力残差模块
super-resolution reconstruction
attention mechanism
expectation maximization
feature-enhanced residual block
EM self-attention residual block
