结合L^1拟合项的Chan-Vese模型

An Improved Chan-Vese Model Integrated with L^1 Fitting Term

为了提高Chan-Vese(CV)模型对椒盐噪声的鲁棒性,提出一个结合L1拟合项的CV模型.首先采用L1拟合和L2拟合的线性组合构造一个新的拟合项,然后通过调整这2个拟合的权重以提升该模型对不同噪声图像分割的灵活性,最后利用交替迭代算法对模型进行求解.采用被不同噪声污染的人造图像和自然图像进行实验的结果表明,该模型对噪声图像可以取得较好的分割结果,并且对于椒盐噪声污染图像的分割,比CV模型、LBF模型和VFCMS模型更具优势.

An improved CV model integrated withL1 fitting term is proposed in this paper to enhance the robustness of the model for salt and pepper noise. First, a new fitting term is defined as a combination of L1 fitting and L2 fitting. Then, by appropriately choosing the weights of fitting, our proposed model allows flexible segmentation under various noise conditions. Finally, an alternating iterative algorithm is employed to solve the model numerically. Experiments on some synthetic and real images contaminated by different kinds of noise demonstrate that the proposed model is effective and robust for noise image segmentation. Moreover, compared with CV model, LBF model and VFCMS model, our model can achieve superior seg-mentation results for image corrupted by salt and pepper noise.