An Image Segmentation Model with Transformed Total Variation
<|reference_start|>An Image Segmentation Model with Transformed Total Variation: Based on transformed $\ell_1$ regularization, transformed total variation (TTV) has robust image recovery that is competitive with other nonconvex total variation (TV) regularizers, such as TV$^p$, $0<p<1$. Inspired by its performance, we propose a TTV-regularized Mumford--Shah model with fuzzy membership function for image segmentation. To solve it, we design an alternating direction method of multipliers (ADMM) algorithm that utilizes the transformed $\ell_1$ proximal operator. Numerical experiments demonstrate that using TTV is more effective than classical TV and other nonconvex TV variants in image segmentation.<|reference_end|>
@article{dayag2024an,
    title={An Image Segmentation Model with Transformed Total Variation},
    author={Elisha Dayag, Kevin Bui, Fredrick Park, and Jack Xin},
    journal={arXiv preprint arXiv:2406.00571},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00571},
    primaryClass={cs.CV cs.NA eess.IV math.NA}
}