Abstract In this paper, a new model of image segmentation is proposed by considering L0 regularizer term. A new energy function is formulated by utilizing Local Gaussian Distribution model based on binary level set function followed by introducing a L0 gradient regularizer as regularizing term. Instead of zero level set, the binary level set function is applied to differentiate between foreground and background regions. The regularization function is used to calculate the interfaces between foreground sub-regions and regularization term L0 which helps to evolve the curve. The proposed energy function is solved using minimization algorithm to achieve promising results in terms of segmentation accuracy. The different sets of experiments are performed on real and medical images. The proposed model provides higher segmentation accuracy results in less computational time compared to the other state-of-the-art models. Further, the results are found to be superior as compared to other existing models in terms of robustness to noise and intensity inhomogeneity.

中文翻译：

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一种使用 L0 正则化器进行图像分割的新二元水平集模型

摘要 本文提出了一种考虑L0正则项的图像分割新模型。通过利用基于二元水平集函数的局部高斯分布模型，然后引入 L0 梯度正则化器作为正则化项，制定了新的能量函数。应用二进制级别集函数来区分前景和背景区域，而不是零级别集。正则化函数用于计算前景子区域和正则化项 L0 之间的接口，这有助于曲线的演化。使用最小化算法求解所提出的能量函数，以在分割精度方面取得有希望的结果。不同的实验集是在真实和医学图像上进行的。与其他最先进的模型相比，所提出的模型以更少的计算时间提供更高的分割精度结果。此外，发现与其他现有模型相比，在对噪声和强度不均匀性的鲁棒性方面，结果更为优越。