基于树状多分支残差注意力网络的真实场景图像超分辨率重构

Real-world super-resolution based on residual attention network with treebased multi-branch structure

真实场景图像超分辨率重构方法的研究进一步推动了超分辨率重构的应用,成为研究的热点.针对现有方法多采用单一输出代表高分辨率图像的高频特征细节,难以保障稳定、准确高频细节的问题,提出一种基于树状多分支残差注意力网络的真实场景图像超分辨率重构方法.该方法通过树状结构形成多分支超分辨率重构网络,增强特征表现能力,进而丰富重构图像高频细节.每条分支采用双通道残差策略对基础块进行连接,允许更多低频特征通过.进一步设计了基础块,融入密集残差结构和注意力机制,可以在加深网络的同时使网络在通道和空间上进行全局信息自适应调整.面对树状分支的多个重构结果,采用空间频率方法进行融合.实验结果表明,同当前先进的同类方法相比,所提出方法具有更佳的重构效果.

The research on real-word super-resolution further promotes the application of super-resolution and becomes a hotspot.A real-world super-resolution method based on residual attention network with tree based multi-branch structure is proposed to solve the problem that a single output is difficult to ensure stable and accurate high-frequency details.Tree based structure is designed to form a multi-branch super-resolution network,which can enhance feature representation capability and enrich high-frequency details of the restored image.A dual residual path schema is proposed for basic blocks,which lets more low frequencies pass.The basic block adopts a dense residual module and attention mechanism to deepen the network and to adaptively adjust global information in both channel and space.Experimental results show that the proposed method can achieve better reconstruction than the current advanced similar methods.