Article
Computer Science, Information Systems
Zhi-Feng Pang, Lin-Lin Fan, Hao-Hui Zhu
Summary: This paper proposes a novel numerical method based on the Lagrangian dual scheme to solve the CV model. By transforming the binary constraint of the level set function into a nonsmooth optimization problem, the Dual-based ADMM is employed to solve this problem. The experimental results show that the proposed method is more robust and accurate than the other three comparison schemes.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Biology
Ping Tang, Yu-qian Zhao, Miao Liao
Summary: The study introduces a fully automatic method for multi-organ segmentation from 3D abdominal CT volumes, utilizing a combination of different algorithms to achieve competitive segmentation results with state-of-the-art methods, demonstrating high efficiency and accuracy.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Engineering, Electrical & Electronic
Samad Wali, Chunming Li, Mudassar Imran, Abdul Shakoor, Abdul Basit
Summary: This paper analyzes and tests the efficiency of the alternating direction method of multipliers (ADMM) for level-set based image segmentation. The comparison with the classical gradient descent method shows the effectiveness and efficiency of the ADMM method. Experimental results on medical image segmentation demonstrate an average segmentation coefficient of 0.97 (Dice) and 0.92 (Jaccard), with an average running time of 1.70 seconds and average estimation values of 0.0932 (MAD), 0.993 (accuracy), 0.981 (sensitivity), and 0.964 (specificity).
Article
Mathematics, Applied
Chaoyu Liu, Zhonghua Qiao, Qian Zhang
Summary: This paper proposes an Allen-Cahn Chan-Vese model for multi-phase image segmentation. By integrating the Allen-Cahn term and the Chan-Vese fitting energy term, an energy functional is established, and its minimum locates the segmentation contour. The subsequent minimization process involves variational calculation on fitting intensities and the solution approximation of several Allen-Cahn equations, where... Allen-Cahn equations are sufficient for partitioning m = 2(n) segments. The derived Allen-Cahn equations are solved using efficient numerical solvers with exponential time integrations and finite difference space discretization. The discrete maximum bound principle and energy stability of the proposed numerical schemes are proven. Finally, the capability of the segmentation method is demonstrated through various experiments on different types of images.
COMPUTERS & MATHEMATICS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Jintao Song, Huizhu Pan, Wanquan Liu, Zisen Xu, Zhenkuan Pan
Summary: The new variational level set model combining various methods effectively segments objects with large occluded boundaries, improving segmentation efficiency and reducing reliance on parameter tuning and initialization.
Article
Computer Science, Artificial Intelligence
Zhang-Lei Shi, Xiao Peng Li, Chi-Sing Leung, Hing Cheung So
Summary: This study introduces an algorithm for portfolio optimization that explicitly controls the cardinality of the portfolio through a non-convex optimization problem. Results on real-world datasets demonstrate the superiority of the proposed algorithm over several existing algorithms.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Biomedical
Saru Meena Ramu, Muthaiah Rajappa, Kannan Krithivasan, Jaikanth Jayakumar, Panagiotis Chatzistergos, Nachiappan Chockalingam
Summary: The study investigates the use of different optimization methods in the Chan-Vese (CV) model, finding that the Barzilai-Borwein projected GD method significantly reduces computational time with minimal structural distortion. The choice of optimization method has a substantial impact on processing time and output quality.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Engineering, Mechanical
Miantao Chao, Liqun Liu
Summary: In this paper, a dynamical ADMM is proposed for two-block separable optimization problems. The classical ADMM is obtained after discretizing the dynamical system in time. Under appropriate conditions, it is proved that the trajectory asymptotically converges to a saddle point of the Lagrangian function. When the coefficient matrices in the constraint are identity matrices, a worst-case O(1/t) convergence rate in the ergodic sense is proven.
NONLINEAR DYNAMICS
(2023)
Article
Ecology
Yamina Boutiche, Abdelhamid Abdesselam, Nabil Chetih, Mohammed Khorchef, Naim Ramou
Summary: This paper proposes a method for detecting vegetation in agricultural images under real field conditions. Experimental results demonstrate that the proposed method outperforms other methods in terms of accuracy, precision, and recall, and it performs well under different field conditions.
ECOLOGICAL INFORMATICS
(2022)
Article
Engineering, Mechanical
Liang Yu, Jerome Antoni, Han Zhao, Qixin Guo, Rui Wang, Weikang Jiang
Summary: In this paper, a novel computational framework for acoustic imaging is proposed, which simplifies the algorithm to balance computation speed and accuracy by converting the problem into an inverse problem and dividing it into forward model and denoising model parts.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Multidisciplinary
Benxin Zhang, Guopu Zhu, Zhibin Zhu, Sam Kwong
Summary: This paper proposes a nonconvex log total variation model for image restoration, and presents a specific alternating direction method of multipliers to solve the model. Experimental results demonstrate that the proposed method is effective in image denoising, deblurring, computed tomography, magnetic resonance imaging, and image super-resolution.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Cell Biology
Aiye Wang, Zhuoqun Zhang, Siqi Wang, An Pan, Caiwen Ma, Baoli Yao
Summary: This paper introduces a method called ADMM-FPM which utilizes the concept of alternating direction method of multipliers to solve the phase retrieval problem in Fourier ptychographic microscopy (FPM). Compared to existing algorithms, ADMM-FPM shows better stability and robustness under noisy conditions.
Article
Operations Research & Management Science
Sedi Bartz, Ruben Campoy, Hung M. Phan
Summary: This paper proposes and studies an adaptive version of ADMM for the case where the objective function is the sum of a strongly convex function and a weakly convex function. By combining generalized notions of convexity and penalty parameters with the convexity constants of the functions, we prove convergence of the algorithm under natural assumptions.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Feng Lin, Weiyu Li, Qing Ling
Summary: This paper addresses the problem of distributed learning under Byzantine attacks and proposes a Byzantine-robust stochastic ADMM method. The effectiveness of the proposed method is demonstrated through theoretical analysis and numerical experiments.
Article
Optics
Roger Chiu, Carlos E. Castaneda, Onofre Orozco-Lopez, Didier Lopez-Mancilla, Edgar Villafana-Rauda
Summary: Keeping information hidden has always been crucial, and cryptography has been used for centuries, with various encryption methods and models proposed. The encryption method is a key challenge, as the security of the encryption system depends on how the process is conducted. In this study, we propose an encryption model based on convolution, which lacks a direct inversion process, making it difficult to restore encrypted data. The results demonstrate that this model is highly resistant to external attacks, making it a promising candidate for information encryption applications.
OPTICS AND LASER TECHNOLOGY
(2023)