Hierarchical Mumford-Shah Model for Image Segmentation,Denoising, and Reconstruction Using an Iterative Tier-by-Tier Algorithm Based on Level Set Methods
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摘要: 针对能够同时进行图像分割、去噪与重建目的的Mumford-Shah能量泛涵最小值图像模型求解非常困难这一问题,提出了"多层Mumford-Shah图像分割、去噪与重建模型"和求解该多层模型最小值的"水平集逐层迭代算法".该多层模型是Mumford-Shah"最小分割问题"的"多层"模型.实验结果表明,该方法不仅能够同时进行具有T型图像边缘或更复杂拓扑结构图像边缘的图像分割、去噪与重建,而且比Tsai A.等人提出的多层求解轮廓和Chan T.等人提出的多相水平集方法更简单更有效.
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关键词:
- Mumford-Shan模型 /
- 水平集方法 /
- 层次模型 /
- 活动轮廓模型 /
- 最小分割问题
Abstract: A novel hierarchical Mumford-Shah functional model is addressed to simultaneously segment,denoise and reconstruct the data within a given image and to handle important image features such as triple points and other multiple junctions, which can be seen as a hierarchical case of the Mumford-Shah minimal partition problem. At the same time, a new iterative tier-by-tier algorithm based on techniques of level set is proposed to minimize the hierarchical Mumford-Shah functional, which is more effective and more simple than existing algorithms such as the hierarchical approach proposed by Tsai A et al. and the multiphase level set methods proposed by Chan T et al.
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