- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Space-variant generalised Gaussian regularisation for image restoration
摘要： We propose a new space-variant regularisation term for variational image restoration based on the assumption that the gradient magnitudes of the target image distribute locally according to a half-Generalised Gaussian distribution. This leads to a highly ?exible regulariser characterised by two per-pixel free parameters, which are automatically estimated from the observed image. The proposed regulariser is coupled with either the L2 or the L1 ?delity terms, in order to e?ectively deal with additive white Gaussian noise or impulsive noises such as, e.g. additive white Laplace and salt and pepper noise. The restored image is e?ciently computed by means of an iterative numerical algorithm based on the alternating direction method of multipliers. Numerical examples indicate that the proposed regulariser holds the potential for achieving high-quality restorations for a wide range of target images characterised by di?erent gradient distributions and for the di?erent types of noise considered.
关键词： alternating direction method of multipliers,half-Generalised Gaussian distribution,Image restoration,variational methods
更新于2025-09-23 15:23:52
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An Outlier-insensitive Unmixing Algorithm with Spatially Varying Hyperspectral Signatures
摘要： Effective hyperspectral unmixing (HU) is essential to the estimation of the underlying materials’ signatures (endmember signatures) and their spatial distributions (abundance maps) from a given image (data) of a hyperspectral scene. Recently, investigating HU under the non-negligible endmember variability (EV) and outlier effects (OE) has drawn extensive attention. Some state-of-the-art works either consider EV or consider OE, but none of them considers both EV and OE simultaneously. In this paper, we propose a novel HU algorithm, referred to as the variability/outlier-insensitive multi-convex unmixing (VOIMU) algorithm, that is robust against both EV and OE. Considering two suitable regularizers, a nonconvex minimization problem is formulated for which the perturbed linear mixing model (PLMM) proposed by Thouvenin et al., is used for modeling EV, while OE is implicitly handled by applying a p quasi-norm to the data fitting with 0 < p < 1. Then we reformulate it into a multi-convex problem which is then solved by the block coordinate decent (BCD) method, with convergence guarantee by casting it into the block successive upper bound minimization (BSUM) framework. The proposed VOIMU algorithm can yield a stationary-point solution with convergence guarantee, together with some intriguing information of potential outlier pixels though outliers are neither physically modeled in the above problem nor detected in the algorithm operation. Finally, we provide some simulation results and experimental results using real data to demonstrate the efficacy and practical applicability of the proposed VOIMU algorithm.
关键词： block successive upper bound minimization (BSUM),endmember variability,alternating direction method of multipliers (ADMM),outlier effects,block coordinate decent (BCD) method,Hyperspectral imaging
更新于2025-09-23 15:22:29
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VLC and D2D Heterogeneous Network Optimization: A Reinforcement Learning Approach Based on Equilibrium Problems with Equilibrium Constraints
摘要： The Radio Frequency (RF) spectrum crunch has triggered the harnessing of other sources of bandwidth, for which visible light is a promising candidate. Even though Visible Light Communication (VLC) ensures high capacity, coverage is limited. This necessitates the integration of VLC and Device-to-Device (D2D) technologies into heterogeneous networks. In particular, mobile users which are accessible by the VLC transmitters can relay data to mobile users which are not, by means of D2D communication. However, due to the distributed behaviors of mobile users, determining optimal data transmission routes from VLC transmitters to end mobile devices is a major challenge. In this paper, we propose a Reinforcement Learning (RL) based approach to determine multi-hop data transmission routes in an indoor VLC-D2D heterogeneous network. We obtain the rewards for the RL based method dynamically, by formulating the interactions between the mobile users relaying the data as an Equilibrium Problem with Equilibrium Constraints (EPEC) and using Alternating Direction Method of Multipliers (ADMM) to solve it. The proposed technique can achieve optimal data transmission routes in a distributed manner. Simulation results demonstrate the effectiveness of the proposed approach, showing that transmission routes with low delays and high capacities can be achieved through the learning algorithm.
关键词： Device-to-Device,Reinforcement Learning,Visible Light Communication,Alternating Direction Method of Multipliers,Heterogeneous Network,Equilibrium Problem with Equilibrium Constraints
更新于2025-09-23 15:22:29
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An Efficient and Fast Quantum State Estimator With Sparse Disturbance
摘要： A pure or nearly pure quantum state can be described as a low-rank density matrix, which is a positive semidefinite and unit-trace Hermitian. We consider the problem of recovering such a low-rank density matrix contaminated by sparse components, from a small set of linear measurements. This quantum state estimation task can be formulated as a robust principal component analysis (RPCA) problem subject to positive semidefinite and unit-trace Hermitian constraints. We propose an efficient and fast inexact alternating direction method of multipliers (I-ADMM), in which the subproblems are solved inexactly and hence have closed-form solutions. We prove global convergence of the proposed I-ADMM, and the theoretical result provides a guideline for parameter setting. Numerical experiments show that the proposed I-ADMM can recover state density matrices of 5 qubits on a laptop in 0.69 s, with 6 × 10^{-4} accuracy (99.38% fidelity) using 30% compressive sensing measurements, which outperforms existing algorithms.
关键词： quantum state estimation (QSE),Alternating direction method of multipliers (ADMM),robust principal component analysis (RPCA)
更新于2025-09-23 15:22:29
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A multilevel color image thresholding scheme based on minimum cross entropy and alternating direction method of multipliers
摘要： In this paper, we focus on the problem of fast color image segmentation and propose a two-stage image segmentation method using multi-level thresholding algorithm and statistical region merging(SRM) technique. With the help of the alternating direction method of multipliers(ADMM), the optimal threshold values can be determined by the centroid of sub-modes of the image histogram that follow minimum cross entropy(MEC). In the second stage, a modified SRM is used to remove over-segmented regions of thresholded image to bring the final result. Experimental results depicts that the proposed approach selects threshold values more efficiently as compared to other MCE-based thresholding techniques and produces high quality of the segmented color images than the other methods like mean-shift, normalized cuts(Ncuts) and differential evolution (Q=7).
关键词： Multi-Level image segmentation,Minimum cross entropy,Color image,Alternating direction method of multipliers.
更新于2025-09-19 17:15:36
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Multi-Agent Distributed Beamforming with Improper Gaussian Signaling for MIMO Interference Broadcast Channels
摘要： For rate optimization in interference limited network, improper Gaussian signaling has shown its capability to outperform the conventional proper Gaussian signaling. In this work, we study a weighted sum-rate maximization problem with improper Gaussian signaling for the multiple-input multiple-output interference broadcast channel (MIMO-IBC). To solve this nonconvex and NP-hard problem, we propose an effective separate covariance and pseudo-covariance matrices optimization algorithm. In the covariance optimization, a weighted minimum mean square error (WMMSE) algorithm is adopted, and, in the pseudo-covariance optimization, an alternating optimization (AO) algorithm is proposed, which guarantees convergence to a stationary solution and ensures a sum-rate improvement over proper Gaussian signaling. An alternating direction method of multipliers (ADMM)-based multi-agent distributed algorithm is proposed to solve an AO subproblem with the globally optimal solution in a parallel and scalable fashion. The proposed scheme exhibits favorable convergence, optimality, and complexity properties for future large-scale networks. Simulation results demonstrate the superior sum-rate performance of the proposed algorithm as compared to existing schemes with proper as well as improper Gaussian signaling under various network configurations.
关键词： Multiple-input multiple-output interference broadcast channel (MIMO-IBC),alternating direction method of multipliers (ADMM),signaling,cloud radio access network (C-RAN),distributed beamforming,improper multi-agent optimization
更新于2025-09-09 09:28:46
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Total variation and high-order total variation adaptive model for restoring blurred images with Cauchy noise
摘要： In this paper, we propose a novel model to restore an image corrupted by blur and Cauchy noise. The model is composed of a data fidelity term and two regularization terms including total variation and high-order total variation. Total variation provides well-preserved edge features, but suffers from staircase effects in smooth regions, whereas high-order total variation can alleviate staircase effects. Moreover, we introduce a strategy for adaptively selecting regularization parameters. We develop an efficient alternating minimization algorithm for solving the proposed model. Numerical examples suggest that the proposed method has the advantages of better preserving edges and reducing staircase effects.
关键词： Total variation and high-order total variation,Image restoration,Alternating direction method of multipliers,Cauchy noise,Adaptive regularization parameters
更新于2025-09-09 09:28:46