A two-stage image reconstruction strategy for electrical impedance tomography

Electrical impedance tomography (EIT) is a potential and promising tomographic technique. Based on a reconstruction strategy, conductivity distribution can be imaged by processing boundary measurements. It should be noticed that the process of image reconstruction involves the solution of a nonlinear ill-posed inverse problem. To tackle this problem, a novel two-stage image reconstruction strategy is proposed in this work. It combines the advantages of total generalized variation regularization method and tight wavelet approach. The solution of the proposed method is acquired by employing alternating minimization algorithm and spilt Bregman algorithm. In the numerical simulation, reconstruction of five models is studied. Aside from the visual observation, we have also validated the proposed method with quantitative comparison. Meanwhile, the impact of noise on the reconstruction is considered. Furthermore, the proposed method is evaluated by phantom experimental data. The simulation and experimental results have demonstrated the superior performance of the proposed method in visualizing conductivity distribution.

Second-order total generalized variation based model for restoring images with mixed Poisson — Gaussian noise

Introduction: A common problem in image restoration is image denoising. Among many noise models, the mixed Poisson-Gaussian model has recently aroused considerable interest. Purpose: Development of a model for denoising images corrupted by mixed Poisson-Gaussian noise, along with an algorithm for solving the resulting minimization problem. Results: We proposed a new total variation model for restoring an image with mixed Poisson-Gaussian noise, based on second-order total generalized variation. In order to solve this problem, an efficient alternating minimization algorithm is used. To illustrate its comparison with related methods, experimental results are presented, demonstrating the high efficiency of the proposed approach. Practical relevance: The proposed model allows you to remove mixed Poisson-Gaussian noise in digital images, preserving the edges. The presented numerical results demonstrate the competitive features of the proposed model.

Initial Value Acceleration-Based Alternating Minimization Algorithm for Dynamic Sub-Connected Hybrid Precoding in Millimeter Wave MIMO Systems

Symmetry-based sub-connected hybrid precoding is an energy-friendly structure in wireless communications. Most of the prior work set a diagonal constraint on the analog precoder and used a randomly set matrix as the initial analog precoder, which did not match the optimal channel conditions, leading to a decrease in spectral efficiency, and some had huge complexity when calculating the digital precoder. Aiming to solve these problems, this paper proposed a low-complexity hybrid precoding algorithm based on Initial value Acceleration-based Alternating Minimization (IAAM). Leveraging the special structure of analog precoder in sub-connected scheme, we design the analog precoder through low-complexity quadratic programming and use the least square method to obtain the digital precoder. Moreover, we design a heuristic algorithm with the objective function of maximizing the effective channel gain to calculate the initial analog precoder as the starting point for alternating minimization. The simulation results show that the spectral efficiency of this algorithm is at least 17.5% higher than the existing two traditional sub-connected algorithms. Additionally, it increases energy efficiency by at least 12.8% compa with the Orthogonal Matching Pursuit (OMP) algorithm. Its algorithm convergence speed is fast, which increases with the number of RF chains.

Preserving Ordinal Consensus: Towards Feature Selection for Unlabeled Data

To better pre-process unlabeled data, most existing feature selection methods remove redundant and noisy information by exploring some intrinsic structures embedded in samples. However, these unsupervised studies focus too much on the relations among samples, totally neglecting the feature-level geometric information. This paper proposes an unsupervised triplet-induced graph to explore a new type of potential structure at feature level, and incorporates it into simultaneous feature selection and clustering. In the feature selection part, we design an ordinal consensus preserving term based on a triplet-induced graph. This term enforces the projection vectors to preserve the relative proximity of original features, which contributes to selecting more relevant features. In the clustering part, Self-Paced Learning (SPL) is introduced to gradually learn from ‘easy’ to ‘complex’ samples. SPL alleviates the dilemma of falling into the bad local minima incurred by noise and outliers. Specifically, we propose a compelling regularizer for SPL to obtain a robust loss. Finally, an alternating minimization algorithm is developed to efficiently optimize the proposed model. Extensive experiments on different benchmark datasets consistently demonstrate the superiority of our proposed method.