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Image Segmentation with B-Spline Level Set
Published in Ayman El-Baz, Jasjit S. Suri, Level Set Method in Medical Imaging Segmentation, 2019
Shenhai Zheng, Bin Fang, Laquan Li
In this subsection, we will apply the Split Bregman method to minimize the proposed convex model of u. The split Bregman method is an efficient technique to L1-regularized optimization problems, such as TV denoising and compressed sensing (CS) problems [47].
Noise reduction method based on curvelet theory of seismic data
Published in Petroleum Science and Technology, 2022
Siwei Zhao, Dayong Zhen, Xiaokang Yin, Fangbo Chen, Ibrar Iqbal, Tianyu Zhang, Mingkun Jia, Siqin Liu, Jie Zhu, Ping Li
From this equation, the suppression of noisy seismic data can be guaranteed during each iteration. It can be seen that the linear Bregman method is very simple, and does not involve many adjustment parameters. There are only two lines in the linearized Bregman method’s main steps. As a result, this method is relatively simple, and does not necessitate a large number of tuning parameters. The linearized Bregman method is also much easier to implement than other methods, for example, the spectral projected-gradient for L1 norm (SPGL1) (Han, Han, and Li 2012). Using the same computing resources, the much simpler linearized Bregman method outperforms the SPGL1.
Automatic liver tumour segmentation in CT combining FCN and NMF-based deformable model
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2020
Shenhai Zheng, Bin Fang, Laquan Li, Mingqi Gao, Yi Wang, Kaiyi Peng
Next, we will apply the Split Bregman method (Goldstein and Osher 2009) to minimize the convex model (7) of . Introduce the auxiliary variable such that , the elegant two-phase Split Bregman iteration is given as follows: