流场置信度引导与各向异性约束的无监督图像拼接方法

doi:10.11996/JG.j.2095-302X.2026040683

图学学报 ›› 2026, Vol. 47 ›› Issue (4): 683-694.DOI: 10.11996/JG.j.2095-302X.2026040683

• 图像处理与计算机视觉 • 上一篇下一篇

流场置信度引导与各向异性约束的无监督图像拼接方法

田硕¹, 黄炎¹, 齐家望¹, 石超君¹^,²^,³, 戚银城¹^,²^,³()

¹ 华北电力大学电子与通信工程系，河北保定 071003
² 华北电力大学河北省电力物联网技术重点实验室，河北保定 071003
³ 华北电力大学电力物联智慧化技术河北省工程研究中心，河北保定 071003

收稿日期:2026-03-25 接受日期:2026-06-10 出版日期:2026-08-31 发布日期:2026-08-31
通讯作者:戚银城，E-mail：qiych@ncepu.edu.cn
基金资助:
国家自然科学基金(62206095)

Unsupervised image stitching method guided by flow field confidence and anisotropic constraints

TIAN Shuo¹, HUANG Yan¹, QI Jiawang¹, SHI Chaojun¹^,²^,³, QI Yincheng¹^,²^,³()

¹ Department of Electronic and Communication Engineering, North China Electric Power University, Baoding Hebei 071003, China
² Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding Hebei 071003, China
³ Hebei Engineering Research Center of Intelligent Technology for Power Internet of Things, North China Electric Power University, Baoding Hebei 071003, China

Received:2026-03-25 Accepted:2026-06-10 Published:2026-08-31 Online:2026-08-31
Contact: QI Yincheng，E-mail：qiych@ncepu.edu.cn
Supported by:
National Natural Science Foundation of China(62206095)

摘要/Abstract

摘要：

针对现有无监督深度图像拼接方法在大视差场景下易产生非刚性形变问题，提出一种流场置信度引导与各向异性结构约束的无监督图像拼接方法，旨在缓解深度图像拼接中对齐精度与结构完整性之间的冲突，实现大视差场景下的图像拼接。首先，设计流场置信度估计模块(FCEM)，对上下文相关层输出的密集对应流场进行置信度加权，引导网络优先拟合纹理丰富区域的可靠位移信号，同时主动抑制低置信度区域的误匹配干扰，提升网格变形的可靠性。其次，提出各向异性几何结构约束(AGSC)，利用索贝尔边缘响应构建自适应权重，将传统的各向同性平滑约束解耦为网格平滑项与垂直曲率项，在垂直方向施加曲率惩罚来保持直线度，同时释放水平方向的变形自由度，在对齐精度与结构约束之间取得平衡。UDIS-D数据集上的实验结果表明，在维持主流深度拼接方法同等对齐能力的同时，全测试集直线拟合误差(LRMSE)降低28.1%，共线性误差下降低33.4%，有效改善了拼接图像垂直结构弯曲形变。147对多场景图像拼接数据集上的实验结果表明，LRMSE较基线模型降低了5.8%，共线性误差降低了27.0%。客观指标与可视化拼接结果均表现出垂直结构保持优势，证实了方法的几何结构保持能力与跨场景泛化能力。

关键词: 图像拼接, 网格变形, 流场置信度估计, 各向异性几何结构约束, 几何结构保持

Abstract:

To address the non-rigid deformation challenges faced by existing unsupervised deep image stitching methods in large-parallax scenes, an unsupervised image stitching approach guided by flow-field confidence and constrained by anisotropic geometric structure was proposed. The goal was to mitigate the conflict between alignment accuracy and structural integrity in deep image stitching and to enable image stitching in large-parallax scenes. First, a Flow Confidence Estimation Module (FCEM) was designed to assign confidence weights to the dense correspondence flow field generated by the context-aware layer. This module guided the network to prioritize reliable displacement signals in texture-rich regions while actively suppressing mismatches in low-confidence areas, thereby enhancing the reliability of mesh deformation. Second, an Anisotropic Geometric Structure Constraint (AGSC) was proposed, which constructed adaptive weights using Sobel edge responses and decoupled the traditional isotropic smoothness constraint into a mesh-smoothing term and a vertical-curvature term. By applying curvature penalties in the vertical direction to preserve straight-line structures while allowing horizontal deformation flexibility, the method achieves a balance between alignment accuracy and structural constraints. Experimental results on the UDIS-D dataset demonstrated that, while maintaining alignment performance comparable to that of mainstream deep stitching methods, the proposed method reduced the Line-fitting Root Mean Square Error (LRMSE) by 28.1% and the collinearity error (Eerr) by 33.4% across the full test set, effectively alleviating vertical structural bending in stitched images. Additional experiments on a 147-pair multi-scene image stitching dataset showed reductions in LRMSE and Eerr of 5.8% and 27.0%, respectively, compared to the baseline model. Both objective metrics and visual stitching results confirm the superiority of the proposed method in preserving vertical structures, verifying its geometric structure-preserving capability and cross-scene generalization performance.

Key words: image stitching, mesh deformation, flow confidence estimation, anisotropic geometric structure constraint, geometric structure preservation

中图分类号:

田硕, 黄炎, 齐家望, 石超君, 戚银城. 流场置信度引导与各向异性约束的无监督图像拼接方法[J]. 图学学报, 2026, 47(4): 683-694.

TIAN Shuo, HUANG Yan, QI Jiawang, SHI Chaojun, QI Yincheng. Unsupervised image stitching method guided by flow field confidence and anisotropic constraints[J]. Journal of Graphics, 2026, 47(4): 683-694.

图/表 11

图1 UDIS++垂直结构形变现象

Fig. 1 Vertical structure distortion in UDIS++

图2 本文方法总体框架示意图

Fig. 2 Overview of the proposed image stitching framework

图3 UDIS-D数据集中的代表性场景图像对

Fig. 3 Representative image pairs of typical scenarios in the UDIS-D dataset

表1 不同方法的对齐质量对比

Table 1 Alignment quality comparison of different methods

方法	PSNR				SSIM
方法	简单	中等	困难	平均值	简单	中等	困难	平均值
SIFT+RANSAC	28.57	24.08	18.55	23.27	0.916	0.833	0.636	0.779
APAP	27.96	24.39	20.21	23.79	0.901	0.837	0.682	0.794
LPC	26.94	22.63	19.31	22.59	0.878	0.764	0.610	0.736
UDIS	25.16	20.96	18.36	21.17	0.834	0.669	0.495	0.648
UDIS++	30.19	25.84	21.57	25.43	0.933	0.875	0.739	0.838
DSFN	30.20	25.85	21.65	25.46	0.933	0.875	0.742	0.838
本文方法	29.95	25.54	21.21	25.12	0.931	0.871	0.732	0.831

图4 拼接结果可视化对比图

Fig. 4 Visual comparison of stitching results

表2 同等对齐强度下的几何一致性评估

Table 2 Geometric consistency evaluation under equivalent alignment strength

方法	PSNR 均值	SSIM 均值	LRMSE
方法	PSNR 均值	SSIM 均值	简单	中等	困难	均值
UDIS++	25.12	0.830	0.25	0.28	0.34	0.30
本文方法	25.12	0.831	0.20	0.24	0.31	0.26

图5 分场景结构保持指标柱状图对比

Fig. 5 Comparison of structure preservation metrics across different scenes ((a) Eerr; (b) Edis; (c) Edir)

表3 消融实验结果

Table 3 Ablation study results

方法	模块		指标
方法	FCEM	AGSC	PSNR	SSIM	LRMSE
UDIS++	×	×	25.43	0.836	0.359
UDIS++	√	×	25.39	0.837	0.308
UDIS++	×	√	25.06	0.826	0.296
本文方法	√	√	25.12	0.831	0.258

表4 在147对多场景数据集上的性能比较

Table 4 Performance Comparison on the 147-Pair Multi-Scene Dataset

方法	PSNR 均值	SSIM 均值	LRMSE
方法	PSNR 均值	SSIM 均值	简单	中等	困难	均值
UDIS++	20.12	0.712	0.31	0.39	0.70	0.49
本文方法	20.04	0.706	0.27	0.31	0.71	0.46

表5 147对多场景数据集上的结构保持指标比较

Table 5 Comparison of Structure Preservation Metrics on the 147-Pair Multi-Scene Dataset

方法	均值
方法	E_err/像素	E_dis/像素	E_dir
UDIS++	0.367	139.068	0.135
本文方法	0.268	137.564	0.133

图6 跨数据集拼接结果可视化对比图((a),(d) 书架; (b),(e) 建筑立面; (c),(f) 图书馆)

Fig. 6 Visual comparison of stitching results on the cross-dataset ((a),(d) Bookshelves; (b),(e) Building facades; (c),(f) Library)

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流场置信度引导与各向异性约束的无监督图像拼接方法

Unsupervised image stitching method guided by flow field confidence and anisotropic constraints

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