A Two-Stage Image Segmentation Method for Blurry Images with Poisson or Multiplicative Gamma Noise
A Two-Stage Image Segmentation Method for Blurry Images with Poisson or Multiplicative Gamma Noise
复制标题
具有泊松或乘性伽玛噪声的模糊图像的两阶段图像分割方法
DOI：
10.1137/130920241
复制
发表时间：
2014-01-01
影响因子：
2.1
通讯作者：
Zeng, Tieyong
中科院分区：
文献类型：
--
作者：
Chan, Raymond;Yang, Hongfei;Zeng, Tieyong
本文提出了一种在存在泊松或乘性伽玛噪声的情况下分割模糊图像的两阶段方法。该方法的灵感来自以前的工作，两阶段分割和使用的I-发散项来处理噪声。我们的方法的第一阶段是找到一个光滑的解决方案$u$到一个凸变量的芒福德-沙阿模型，其中的数据保真度项被替换为一个I-发散项。采用原对偶算法有效地解决了最小化问题。我们证明了算法的收敛性和解$u$的唯一性。一旦获得$u$，在第二阶段中，通过将$u$阈值化为不同的相位来完成分割。阈值可以由用户给定，也可以通过使用任何聚类方法自动获得。在我们的方法中，我们可以通过在找到$u$之后选择$（K-1）$阈值来获得任意$K$相位分割（$K\geq 2$）。改变$K$或阈值不需要重新计算$u$。专家...
In this paper, a two-stage method for segmenting blurry images in the presence of Poisson or multiplicative Gamma noise is proposed. The method is inspired by a previous work on two-stage segmentation and the usage of an I-divergence term to handle the noise. The first stage of our method is to find a smooth solution $u$ to a convex variant of the Mumford--Shah model where the $\ell_2$ data-fidelity term is replaced by an I-divergence term. A primal-dual algorithm is adopted to efficiently solve the minimization problem. We prove the convergence of the algorithm and the uniqueness of the solution $u$. Once $u$ is obtained, in the second stage, the segmentation is done by thresholding $u$ into different phases. The thresholds can be given by the users or can be obtained automatically by using any clustering method. In our method, we can obtain any $K$-phase segmentation ($K\geq 2$) by choosing $(K-1)$ thresholds after $u$ is found. Changing $K$ or the thresholds does not require $u$ to be recomputed. Exper...