基于对偶回归和注意力机制的图像超分辨率重建网络  

Image Super-resolution Reconstruction Network Based on Dual Regression and Attention Mechanism

作　　者：印珏泽 周宁宁[1] YIN Jue-Ze;ZHOU Ning-Ning(School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]南京邮电大学计算机学院,南京210023

出　　处：《计算机系统应用》2023年第2期111-118,共8页Computer Systems & Applications

摘　　要：针对单幅图像超分辨率(single image super-resolution,SISR)重建算法存在低分辨率图像(LR)到高分辨率图像(HR)的映射学习具有不适定性,深层神经网络收敛慢且缺乏对高频信息的学习能力以及在深层神经网络传播过程中图像特征信息存在丢失的问题.本文提出了基于对偶回归和残差注意力机制的图像超分辨率重建网络.首先,通过对偶回归约束映射空间.其次,融合通道和空间注意力机制构造了残差注意力模块(RCSAB),加快模型收敛速度的同时,有效增强了对高频信息的学习.最后,融入密集特征融合模块,增强了特征信息流动性.在Set5、Set14、BSD100、Urban100四种基准数据集上与目前主流的单幅图像超分辨率算法进行对比,实验结果表明该方法无论是在客观质量评价指标还是主观视觉效果均优于对比算法.The single image super-resolution(SISR)reconstruction algorithm is ill-posed in the mapping learning from low-resolution(LR)image to high-resolution(HR)image,and the deep neural network has slow convergence and lacks the ability to learn high-frequency information.Moreover,image feature information tends to be lost during deep neural network propagation.In order to address these issues,this study proposes an image super-resolution reconstruction network based on dual regression and residual attention mechanism.Firstly,the mapping space is constrained by dual regression.Secondly,a residual attention module(RCSAB)is constructed by combining channel and spatial attention mechanisms,which not only accelerates the model convergence speed and effectively strengthens the learning of highfrequency information.Finally,a dense feature fusion module is introduced to enhance the fluidity of feature information.In addition,a comparison with the mainstream SISR algorithms is carried out on four benchmark datasets,namely,Set5,Set14,BSD100,and Urban100,and experimental results demonstrate that the proposed method is superior to other algorithms in terms of objective evaluation metrics and subjective visual effects.

关 键 词：单幅图像超分辨率 通道注意力 空间注意力 对偶回归 密集特征融合 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]