基于维度感知注意力的无监督图像拼接网络  

Unsupervised image stitching network based on dimension-aware attention

作　　者：潘杨[1] 王白阳 朱磊[1] 王慧栋 李雪 PAN Yang;WANG Baiyang;ZHU Lei;WANG Huidong;LI Xue(School of Electronics and Information,Xi’an Polytechnic University,Xi’an 710048)

机构地区：[1]西安工程大学电子信息学院,陕西西安710048

出　　处：《西安工程大学学报》2025年第2期93-101,共9页Journal of Xi’an Polytechnic University

基　　金：国家自然科学基金(61971339)。

摘　　要：为改善无监督图像拼接图像常存在的结构变形和错位问题,提出了一种基于维度感知注意力的无监督图像拼接网络(dimension-aware images stitching network,DAISNet)。该网络由单应性估计和重建2个子网络构成,重建子网络又由低分辨率优化分支和高分辨率双路分支组成。引入空洞空间金字塔池化模块和维度感知注意力模块构建低分辨率优化分支,增强对结构特征和拼接边界等关键区域的感知能力;借鉴异构架构思想,通过添加下层子网络构建高分辨率双路分支,提取更多的互补结构信息以改善拼接图像局部细节。实验结果表明:与UDIS等先进图像拼接方法相比,提出的DAISNet方法在UDIS-D数据集上有效改善了拼接图像中的结构变形和错位现象,结构相似性提高了0.63%以上,峰值信噪比提高了0.30%以上。To address the common issues of structural deformation and misalignment in unsupervised image stitching,a dimension-aware images stitching network(DAISNet)based on dimension aware attention was proposed.This network consists of two sub networks:homography estimation and reconstruction.The reconstruction sub network was further composed of two branches:a low-resolution optimization branch and a high-resolution dual channel branch.We introduced the hollow space pyramid pooling module and dimension aware attention module to construct a low-resolution optimization branch,enhancing the perception ability of key areas such as structural features and stitching boundaries.Drawing on the idea of heterogeneous architecture,a high-resolution dual branch was constructed by adding lower-level subnetworks to extract more complementary structural information and improve local details in stitched images.The experimental results show that compared with advanced image stitching methods such as UDIS,the proposed DAISNet method effectively improves the structural deformation and misalignment phenomena in stitched images on the UDIS-D dataset,increases structural similarity by more than 0.63%,and improves peak signal-to-noise ratio by more than 0.30%.

关 键 词：图像拼接 单应性估计 维度感知注意力 低分辨率优化分支 高分辨率双路分支 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]