Robust image segmentation model based on binary level set
This work addresses noise robustness in image segmentation for computer vision applications, but it appears incremental as it builds on existing level set methods with specific enhancements.
The paper tackled the problem of improving robustness to noise in image segmentation by modeling illumination in intensity inhomogeneity images and incorporating a binary level set model, resulting in better segmentation capability on noisy images as demonstrated on various datasets.
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.