基于自适应双分支的图像超分辨率重建算法  

Image Super-Resolution Reconstruction Algorithm Based on Adaptive Two-Branch Block

作　　者：张艳 孙明磊 孙叶美 徐富杰 Zhang Yan;Sun Minglei;Sun Yemeit;Xu Fujie(School of Computer and Information Engineering,Tianjin Chengjian University,Tianjin 300384,China)

机构地区：[1]天津城建大学计算机与信息工程学院,天津300384

出　　处：《激光与光电子学进展》2024年第10期331-341,共11页Laser & Optoelectronics Progress

基　　金：天津市科技特派员项目(20YDTPJC01310)。

摘　　要：近年来,注意力机制广泛应用于图像超分辨率重建,极大地提高了重建网络的性能。为了更有效地利用注意力机制,提出一种基于自适应双分支的图像超分辨率重建算法。该算法设计的自适应双分支模块包括注意力分支和无注意力分支,通过自适应权重层动态平衡双分支的权重,舍弃冗余特征以使两个分支达到自适应平衡;其次,设计通道重组坐标注意力模块,通过通道重组的方式实现跨组特征交互,关注不同网络层特征之间的相关性;最后,设计双层残差聚合模块,构建两层嵌套的残差结构,提取残差块中的深层特征,更有效地提高网络的特征提取能力,提高重建图像的质量。在标准数据集上的大量实验验证了所提方法具有更好的重建效果。Recently,attention mechanisms have been widely applied for image super-resolution reconstruction,substantially improving the reconstruction network's performance.To maximize the effectiveness of the attention mechanisms,this paper proposes an image super-resolution reconstruction algorithm based on an adaptive two-branch block.This adaptive two-branch block designed using the proposed algorithm includes attention and nonattention branches.An adaptive weight layer would dynamically balance the weights of these two branches while eliminating redundant attributes,thereby ensuring an adaptive balance between them.Subsequently,a channel shuffle coordinate attention block was designed to achieve a cross-group feature interaction to focus on the correlation between features across different network layers.Furthermore,a double-layer residual aggregation block was designed to enhance the feature extraction performance of the network and quality of the reconstructed image.Additionally,a double-layer nested residual structure was constructed for extracting deep features within the residual block.Extensive experiments on standard datasets show that the proposed method has a better reconstruction effect.

关 键 词：图像处理 超分辨率重建 自适应权重 通道重组 残差聚合 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]