The images used in various practices are often disturbed by noise, such as Gaussian noise, speckled noise, and salt and pepper noise. Images with noise are one of the challenges for segmentation, since the noise may cause inaccurate segmented results. To cope with the effect of noise on images during segmentation, a novel active contour model is proposed in this paper. The newly proposed model consists of fitting term, regularization term and penalty term. The fitting term is designed using a Gaussian kernel function and fractional order differentiation with an adaptively defined fractional order, which applies different orders to different pixels. The regularization term is applied to maintain the smoothness of curves. In order to ensure stable evolution of curves, a penalty term is added into the proposed model. Comparison experiments are conducted to show the effectiveness and efficiency of the proposed model.

中文翻译：

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基于自适应分数阶微分的新型噪声图像分割主动轮廓模型


各种实践中使用的图像经常受到噪声的干扰，例如高斯噪声、斑点噪声、椒盐噪声等。带有噪声的图像是分割的挑战之一，因为噪声可能会导致分割结果不准确。为了应对分割过程中噪声对图像的影响，本文提出了一种新颖的活动轮廓模型。新提出的模型由拟合项、正则化项和惩罚项组成。拟合项是使用高斯核函数和具有自适应定义的分数阶的分数阶微分来设计的，该分数阶对不同的像素应用不同的阶。应用正则化项来保持曲线的平滑度。为了确保曲线的稳定演化，在所提出的模型中添加了惩罚项。进行了比较实验以证明所提出模型的有效性和效率。