| 引用本文: | 薛怀琦,王双园,张玉荣,姚志远.内窥镜视频关键帧获取及三维重建方法研究(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2026，43（1）：55-63 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 鉴于内窥镜在获取人体内部图像时所面临的数量和质量限制,提出一种新的高清晰度内窥镜视频帧
抽取策略三维重建方法。 方法 首先,采用基于结构相似性指数(SSIM)和 Laplace 算子复合方法筛选和提取具有高
清晰度和低噪声的关键帧;然后,利用基于 Neighbor2Neighbor 深度学习方法对初步的关键帧队列进行处理,减少图
像中的噪声并增强其质量,并利用 COLMAP 增量式 SFM 技术将优质关键帧转化为稀疏点云数据;最后,运用
OpenMVS 深度图融合技术进行稠密重建和曲面拟合,从而获得高精度的三维模型。 结果 实验结果表明:所提方法
不仅显著增强了图像特征匹配度,而且大大提升了三维重建的准确性和精度。 结论 该方法不仅显著提高了图像特
征匹配的精确度,而且提升了三维重建的准确性和精确度,预计将为临床医生提供更直观的诊断依据。 |
| 关键词: 内窥镜图像 Neighbor2Neighbor 增量式 SFM 深度图融合算法 |
| DOI： |
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| Research on Keyframe Acquisition and 3D Reconstruction Methods for Endoscopic Videos |
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XUE Huaiqi WANG Shuangyuan ZHANG Yurong YAO Zhiyuan
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School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai 200093 China
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| Abstract: |
| Objective In view of the limitations in quantity and quality when endoscopes acquire internal human body
images this study proposes a new high-definition endoscope video frame extraction strategy for 3D reconstruction.
Methods First a composite method based on the Structural Similarity Index Measure SSIM and the Laplace operator
was used to screen and extract key frames with high definition and low noise. Then the Neighbor2Neighbor deep-learning
method was used to process the preliminary key-frame queue to reduce noise in the images and enhance their quality. The
COLMAP incremental Structure-from-Motion SFM technique was employed to convert high-quality key frames into
sparse point-cloud data. Finally the OpenMVS depth-map fusion technology was used for dense reconstruction and surface
fitting to obtain a high-precision three-dimensional model. Results The experimental results showed that the proposed
method not only significantly enhanced the image feature matching degree but also greatly improved the accuracy and
precision of three-dimensional reconstruction. Conclusion This method not only significantly improves the accuracy of
image feature matching but also enhances the accuracy and precision of three-dimensional reconstruction. It is expected to
provide clinicians with a more intuitive diagnostic basis. |
| Key words: endoscopic image Neighbor2Neighbor incremental SFM depth-map fusion algorithm |
