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.

翻译：为提升传统图像分割模型对噪声的鲁棒性，本文对强度不均匀图像中的光照项进行建模。此外，为增强模型对含噪图像的鲁棒能力，我们将二值水平集模型融入所提模型中。与传统水平集相比，二值水平集无需持续重新初始化。同时，通过引入变分算子GL，本模型在分割噪声图像方面展现出更优性能。最后采用三步分裂算子法进行求解，并在多种图像上验证了所提模型的有效性。