In order to improve the robustness of traditional image segmentation models
to noise, this paper models the illumination term in intensity inhomogeneity
images. Additionally, to enhance the model's robustness to noisy images, we
incorporate the binary level set model into the proposed model. Compared to the
traditional level set, the binary level set eliminates the need for continuous
reinitialization. Moreover, by introducing the variational operator GL, our
model demonstrates better capability in segmenting noisy images. Finally, we
employ the three-step splitting operator method for solving, and the
effectiveness of the proposed model is demonstrated on various images.

为了提高传统图像分割模型对噪声的鲁棒性，本文模拟了不均匀强度图像中的光照项。此外，为了增强模型对噪声图像的鲁棒性，我们将二值水平集模型融入到所提出的模型中。与传统的水平集相比，二值水平集消除了连续重新初始化的需求。此外，通过引入变分算子 GL，我们的模型在分割嘈杂图像方面展现出更好的能力。最后，我们采用三步分裂算子方法进行求解，并在各种图像上证明了所提出模型的有效性。