【摘要】 Chan-Vese(CV)模型基于图像的全局信息,对噪声有一定的鲁棒性,但是对于强噪声污染图像,CV模型并不能取得好的分割效果。笔者结合变分图像分解和CV模型,提出了一个新的图像分割变分模型。该模型结合BV-L~2分解和CV模型,可以实现噪声图像的同时去噪与分割。采用交替迭代算法对新模型进行求解。以人造图像和自然图像为实验对象验证了研究模型分割的有效性和鲁棒性。此外,对比实验结果显示对于强噪声污染图像,与经典的CV模型和VFCMS模型相比,研究模型在分割质量上有一定优势。更多还原

【Abstract】 Chan-Vese(CV)model is robust to noise to some extent due to the using of global image information.But for the image corrupted by strong noise, CV model cannot get satisfactory segmentation.In this paper,we propose a variational level set model that combines CV model with variational image decomposition.The proposed model integrating BV-L~2 decomposition into CV functional can achieve image denoising and segmentation,simultaneously.An alternative and iterative algorithm is applied to numerically solve the proposed model.Experiments on some synthetic and real images demonstrate the efficiency and robustness of the proposed model.Moreover,compared with the wellknown CV model and VFCMS model,the proposed model can get better performance for the image corrupted by strong noise.更多还原