Image Segmentation via Fischer-Burmeister Total Variation and Thresholding
Image Segmentation via Fischer-Burmeister Total Variation and Thresholding
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DOI：
10.4208/aamm.oa-2021-0126
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发表时间：
2022-06
影响因子：
1.4
通讯作者：
Tingting Wu;Yichen Zhao;Zhihui Mao;Li Shi;Zhiang Li null;Yonghua Zeng
中科院分区：
文献类型：
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作者：
Tingting Wu;Yichen Zhao;Zhihui Mao;Li Shi;Zhiang Li null;Yonghua Zeng
.图像分割是图像处理中的一个重要问题。在本文中，我们提出了一个新的两阶段计划分割的基础上Fischer-Burmeister全变差（FBTV）。我们的方法的第一阶段是从FBTV Mumford-Shah模型计算平滑解。在此基础上，利用半邻近交替方向乘子法（sPADMM）迭代，设计了一种新的凸差分算法（DCA）。在第二阶段，我们利用平滑解和K-均值方法得到分割结果。为了更精确地模拟图像，引入了一个有用的算子，使该模型不仅能够分割噪声或模糊图像，而且能够很好地分割丢失像素的图像。实验结果表明，该方法与现有的一些方法相比，具有更好的分割效果，特别是对含有缺失像素的图像。
. Image segmentation is a significant problem in image processing. In this paper, we propose a new two-stage scheme for segmentation based on the Fischer-Burmeister total variation (FBTV). The first stage of our method is to calculate a smooth solution from the FBTV Mumford-Shah model. Furthermore, we design a new difference of convex algorithm (DCA) with the semi-proximal alternating direction method of multipliers (sPADMM) iteration. In the second stage, we make use of the smooth so-lution and the K-means method to obtain the segmentation result. To simulate images more accurately, a useful operator is introduced, which enables the proposed model to segment not only the noisy or blurry images but the images with missing pixels well. Experiments demonstrate the proposed method produces more preferable re-sults comparing with some state-of-the-art methods, especially on the images with missing pixels.