CAJ | 학술논문

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.
위료제고Chan-Vese(CV)모형대초염조성적로봉성, 제출일개결합L1의합항적CV모형. 수선채용L1의합화L2의합적선성조합구조일개신적의합항, 연후통과조정저 2 개의합적권중이제승해모형대불동조성도상분할적령활성, 최후이용교체질대산법대모형진행구해. 채용피불동조성오염적인조도상화자연도상진행실험적결과표명, 해모형대조성도상가이취득교호적분할결과, 병차대우초염조성오염도상적분할, 비CV모형、LBF모형화VFCMS모형경구우세.
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.