基于拉普拉斯金字塔的区域拼接的多聚焦图像融合方法。
A multi-focus image fusion method via region mosaicking on Laplacian pyramids.
机构信息
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China.
School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China.
出版信息
PLoS One. 2018 May 17;13(5):e0191085. doi: 10.1371/journal.pone.0191085. eCollection 2018.
In this paper, a method named Region Mosaicking on Laplacian Pyramids (RMLP) is proposed to fuse multi-focus images that is captured by microscope. First, the Sum-Modified-Laplacian is applied to measure the focus of multi-focus images. Then the density-based region growing algorithm is utilized to segment the focused region mask of each image. Finally, the mask is decomposed into a mask pyramid to supervise region mosaicking on a Laplacian pyramid. The region level pyramid keeps more original information than the pixel level. The experiment results show that RMLP has best performance in quantitative comparison with other methods. In addition, RMLP is insensitive to noise and can reduces the color distortion of the fused images on two datasets.
本文提出了一种基于拉普拉斯金字塔的区域拼接方法（RMLP），用于融合显微镜拍摄的多聚焦图像。首先，应用和修正拉普拉斯（Sum-Modified-Laplacian）来测量多聚焦图像的焦点。然后，利用基于密度的区域生长算法分割每个图像的聚焦区域掩模。最后，将掩模分解为掩模金字塔来监督拉普拉斯金字塔上的区域拼接。与像素级相比，区域级金字塔保留了更多的原始信息。实验结果表明，RMLP 在与其他方法的定量比较中表现最佳。此外，RMLP 对噪声不敏感，并且可以减少两个数据集上融合图像的颜色失真。
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