论文标题
有效的两阶段图像分割：采用收敛算法的新的非lipschitz分解方法
Effective two-stage image segmentation: a new non-Lipschitz decomposition approach with convergent algorithm
论文作者
论文摘要
图像分割是一个重要的中值视觉主题。对于强度不均匀性的图像的准确有效的多相分割仍然是一个巨大的挑战。我们提出了一种新的两阶段多相分割方法，试图解决此问题，其中关键是计算不均匀性的近似图像。为此，我们建议在第一阶段使用新的非lipschitz变化分解模型。最小化问题是通过迭代支持缩小算法解决的，具有全局收敛保证和迭代序列图像梯度的下限理论。后者表明生成的近似图像（不均匀校正的组件）具有非常整洁的边缘，适用于以下阈值操作。在第二阶段，分割是通过将广泛使用的简单阈值技术应用于分段常数近似来完成的。数值实验表明我们方法在多相分割中的良好收敛性和有效性，用于清洁或嘈杂的均匀和不均匀图像。与某些最新方法的视觉和定量比较都证明了我们基于非lipschitz方法的性能优势。
Image segmentation is an important median level vision topic. Accurate and efficient multiphase segmentation for images with intensity inhomogeneity is still a great challenge. We present a new two-stage multiphase segmentation method trying to tackle this, where the key is to compute an inhomogeneity-free approximate image. For this, we propose to use a new non-Lipschitz variational decomposition model in the first stage. The minimization problem is solved by an iterative support shrinking algorithm, with a global convergence guarantee and a lower bound theory of the image gradient of the iterative sequence. The latter indicates that the generated approximate image (inhomogeneity-corrected component) is with very neat edges and suitable for the following thresholding operation. In the second stage, the segmentation is done by applying a widely-used simple thresholding technique to the piecewise constant approximation. Numerical experiments indicate good convergence properties and effectiveness of our method in multiphase segmentation for either clean or noisy homogeneous and inhomogeneous images. Both visual and quantitative comparisons with some state-of-the-art approaches demonstrate the performance advantages of our non-Lipschitz based method.
