An Improved Mumford-Shah Model and Its Applications to Image Processing with the Piecewise Constant Level Set Method
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摘要: 为了快速的分割和去噪, 经典的 Mumford-Shah 模型需要增强惩罚项的作用, 即增大惩罚项系数, 但是将使目标逐渐的消失. 本文工作提出一个改进的 Mumford-Shah 模型避免了如此现象, 并结合逐段常数水平集方法和梯度下降法求解极小化问题. 并用仿真实验证明了新模型和运算的有效性.
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关键词:
- 分割 /
- 去噪 /
- Mumford-Shah模型 /
- 水平集 /
- PCLSM
Abstract: For quick segmentation and denoising, the classical Mumford-Shah (MS) model needs to enhance the penalization term, i.e. to increase the penalization parameter, which leads to gradual disappearance of objects. In this paper, we propose an improved Mumford-Shah (IMS) model to avoid the phenomenon, and adopt the piecewise constant level set method (PCLSM) and the gradient descent method to solve the minimization problem. Numerical experiments are given to show the efficiency and advantages of the new model and the algorithms.-
Key words:
- Segmentation /
- denoising /
- Mumford-Shah model /
- level set /
- piecewise constant level set method (PCLSM)
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