首页|面向青花瓷碎片图像的U-Net++拼接网络

面向青花瓷碎片图像的U-Net++拼接网络

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  • 万方数据
  • 维普
针对现有图像拼接方法存在拼接处伪影以及非重叠区域内容失真，导致较低的准确性和鲁棒性的问题，提出一种基于U-Net++消除伪影的青花瓷碎片图像拼接方法。首先估计待拼接图像单应性矩阵;然后将单应性矩阵应用于结构拼接阶段，得到图像粗拼接结果;最后以图像粗拼接结果作为先验信息，在内容校正阶段改进现有的U-Net，利用U-Net++细化粗拼接结果，得到最终图像精确拼接。以青花瓷碎片图像数据集与相关经典方法进行实验的结果表明，在3个评价指标中，所提方法的峰值信噪比提高约13％，均方根误差降低约33％，均方误差降低57％左右;该方法具有较小的误差比，不仅能够提高图像拼接质量，而且表现出较好的鲁棒性。
U-Net++Mosaic Network for Blue and White Porcelain Fragment Images
Aiming at the problems of low accuracy and robustness caused by artifacts at the stitching and content distortion in non-overlapping areas in the existing image stitching methods,a blue and white porcelain fragment image stitching method based on U-Net++to eliminate artifacts was proposed.Firstly,the homography matrix of the image to be concatenated is estimated,and then the homography matrix is applied to the structure concatena-tion stage to obtain the rough concatenation result of the image.Finally,the rough concatenation result of the im-age is used as the prior information,and the existing U-Net is improved in the content correction stage,and the rough concatenation result is refined by U-Net++to obtain the final image accurate concatenation.The experi-mental results of the blue and white porcelain fragment image dataset and related classical methods show that,among the three evaluation indicators,the peak signal to noise ratio of the proposed method is increased by about 13％,the root mean square error is reduced by about 33％,and the mean square error is reduced by about 57％.This method can not only improve the quality of image mosaic,but also show good robustness with small error ratio.

image stitchingU-Net++homography matrix estimationcontent correctionblue and white porcelain fragment

张海波、寇姣姣、杨兴、海琳琦、周明全、耿国华

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西北大学信息科学与技术学院 西安 710127

北京师范大学信息科学与技术学院 北京 100875

图像拼接 U-Net++ 单应性矩阵估计 内容校正 青花瓷碎片

国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金陕西省自然科学基金陕西省重点产业链项目青海省重点研发计划

2019YFC15211022019YFC152110361731015619023172019JQ-1662019ZDLSF07-022020-SF-142

2024

10.3724/SP.J.1089.2024.19817

计算机辅助设计与图形学学报
中国计算机学会

计算机辅助设计与图形学学报

CSTPCD北大核心
影响因子：0.892
ISSN：1003-9775
年,卷(期)：2024.36(3)
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