基于卷积神经网络梯度和纹理补偿的单幅图像超分辨率重建  

Super-Resolution Reconstruction of Single Image Based on Convolutional Neural Network Gradient and Texture Compensation

作　　者：黄裕青 李华锋[1] 原铭 张亚飞 HUANG Yuqing;LI Huafeng;YUAN Ming;ZHANG Yafei(School of Information Engineering and Automation,Kunming University of Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,昆明650500

出　　处：《数据采集与处理》2023年第5期1112-1124,共13页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(62161015,61966021)。

摘　　要：现有的单幅图像超分辨率重建算法大都在追求高峰值信噪比(Peak signal-to-noise ratio,PSNR),在特征提取过程中缺少对图像纹理细节信息的关注,导致重建图像的人眼主观感知效果不太理想。为了解决这一问题,本文提出了一种基于卷积神经网络梯度和纹理补偿的单幅图像超分辨率重建算法。具体设计了3条支路分别用于结构特征提取、纹理细节特征提取及梯度补偿,然后利用所提出的融合模块对结构特征和纹理细节特征进行融合。为防止重建过程中丢失图像的纹理信息,提出纹理细节特征提取模块补偿图像的纹理细节信息,增强网络的纹理保持能力;同时,利用梯度补偿模块提取的梯度信息对结构信息进行增强;此外还构建了深层特征提取结构,结合通道注意力与空间注意力对深层特征中的信息进行筛选及特征增强;最后利用二阶残差块对结构和纹理特征进行融合,使重建图像的特征信息更加完善。通过对比实验验证了本文方法的有效性和优越性。The existing super-resolution reconstruction algorithms of single image mostly pursue the peak signal-to-noise ratio(PSNR),and lack the attention to the details of image texture in the process of feature extraction,resulting in poor subjective perception of reconstructed images.In order to solve this problem,this paper proposes a single image super-resolution reconstruction algorithm based on convolutional neural network gradient and texture compensation.Specifically,three branches are designed for structure feature extraction,texture detail feature extraction and gradient compensation,and then the proposed fusion module is used to fuse the structure feature and texture detail feature.To prevent the loss of texture information in the reconstruction process,this paper proposes a texture detail feature extraction module to compensate the texture detail information of the image and enhance the texture retention ability of the network.At the same time,this paper uses the gradient information extracted by the gradient compensation module to enhance the structure information.In addition,this paper also constructs a deep feature extraction structure,combining channel attention and spatial attention to screen and enhance the information in the deep features.Finally,the second-order residual block is used to fuse the structure and texture features,so that the feature information of the reconstructed image is more perfect.The effectiveness and superiority of the proposed method are verified by comparative experiments.

关 键 词：卷积神经网络 超分辨率图像重建 梯度纹理补偿 注意力 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]