Image segmentation is a process of partitioning an image into non-overlapping regions. Existing unsupervised image segmentation methods include level set, automatic thresholding and region-based CV mode and so on. However, image segmentation as a key technology in the field of image processing has not been solved indeed, especially for images with complex texture. For this reason, the authors proposed a novel image segmentation algorithm based on NSST and the vector-valued Chan–Vese (C–V) model. First, they obtained a multi-scale representation by exploiting the non-subsampled shearlet transform (NSST) to extract multi-dimensional data in the image. Afterwards, they gave the vector-valued C–V model, and applied it to all subbands of NSST, which are treated as a vector-valued image. By comparing with other class methods, the experimental results show that the proposed method has better visual effects and lower error rates. But at the same time, it is a little time consuming. The proposed method is reasonable and effective, by taking full advantages of each subband's directional information during its diffusion process, compared with traditional C–V model.

中文翻译：

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基于NSST和矢量值C–V模型的图像分割算法

图像分割是将图像划分为非重叠区域的过程。现有的无监督图像分割方法包括水平集，自动阈值化和基于区域的CV模式等。但是，作为图像处理领域中的关键技术的图像分割确实没有解决，特别是对于纹理复杂的图像。为此，作者提出了一种基于NSST和矢量值Chan-Vese（C-V）模型的新颖图像分割算法。首先，他们通过利用非下采样的小波变换（NSST）提取图像中的多维数据来获得多尺度表示。之后，他们给出了矢量值的C–V模型，并将其应用于NSST的所有子带，这些子带被视为矢量值的图像。通过与其他类方法进行比较，实验结果表明，该方法具有较好的视觉效果和较低的错误率。但是，与此同时，这很耗时。与传统的CV模型相比，该方法在扩散过程中充分利用了每个子带的方向信息，因此是合理有效的。