高级检索
张海波, 寇姣姣, 杨兴, 海琳琦, 周明全, 耿国华. 面向青花瓷碎片图像的U-NET++拼接网络[J]. 计算机辅助设计与图形学学报.
引用本文: 张海波, 寇姣姣, 杨兴, 海琳琦, 周明全, 耿国华. 面向青花瓷碎片图像的U-NET++拼接网络[J]. 计算机辅助设计与图形学学报.
U-net ++ Mosaic network for blue and white porcelain fragment images Zhang Haibo1), Kou Jiaojiao1), Yang Xing1), Hai Linqi1), Zhou Mingquan1,2), and Geng Guohua1)*[J]. Journal of Computer-Aided Design & Computer Graphics.
Citation: U-net ++ Mosaic network for blue and white porcelain fragment images Zhang Haibo1), Kou Jiaojiao1), Yang Xing1), Hai Linqi1), Zhou Mingquan1,2), and Geng Guohua1)*[J]. Journal of Computer-Aided Design & Computer Graphics.

面向青花瓷碎片图像的U-NET++拼接网络

U-net ++ Mosaic network for blue and white porcelain fragment images Zhang Haibo1), Kou Jiaojiao1), Yang Xing1), Hai Linqi1), Zhou Mingquan1,2), and Geng Guohua1)*

  • 摘要: 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, 本文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.

     

    Abstract: Aiming at the problems of low accuracy and robustness caused by artifacts at the stitching and content dis-tortion in non-overlapping areas in the existing image stitching methods, a blue and white porcelain frag-ment image stitching method based on U-Net ++ to eliminate artifacts was proposed. Firstly, the homogra-phy matrix of the image to be concatenated is estimated, and then the homography matrix is applied to the structure concatenation stage to obtain the rough concatenation result of the image. Finally, the rough con-catenation result of the image 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 im-age accurate concatenation. The experimental results of the blue and white porcelain fragment image da-taset and related classical methods show that, among the three evaluation indicators, the peak sig-nal-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.

     

/

返回文章
返回