Search Results (341)

Low-light image enhancement aims to restore visually pleasing normal-light images from degraded low-light observations. Most existing methods handle luminance variation from the enhancement perspective. As a result, the degradation process from a normal-light image to a low-light observation is usually not explicitly characterized. In addition, degradation-oriented optimization is often computationally expensive due to repeated iterative updates. To address these issues, based on the alternating direction method of multipliers (ADMM), a degradation-oriented step-size-consistent unfolding network SCAUNet is proposed. Specifically, a low-light image is modeled as the element-wise product of a normal-light image and a luminance degradation operator, together with additive noise. Based on this formulation, low-light enhancement is converted into the joint estimation of the target image and the degradation operator. Then, a state-based one-step ADMM solver is developed, and a step-size consistency constraint is introduced to improve the reliability of one-step unfolding. Extensive experiments on LOL-v1 and LOL-v2 demonstrate the effectiveness of the proposed SCAUNet. Compared with existing state-of-the-art methods, SCAUNet yields better enhancement quality, especially in preserving image structures, correcting illumination, and suppressing artifacts. Strong generalization ability is also verified on four no-reference low-light datasets, and promising results are obtained on single-image exposure correction. Full article

Microgrids are increasingly recognized as transformative and crucial constituents within advanced smart grid systems. This study introduces a decentralized energy management approach for interconnected microgrids that leverage renewable energy sources such as wind and solar, alongside distributed energy generators and storage mechanisms. An energy coalition manager (ECM) plays a key role in facilitating each microgrid’s integration to optimize power exchanges, enhance data communication, and reduce costs. The alternate-direction multiplier method is adapted to address optimization challenges, incorporating modifications to develop a censored version that enhances communication efficacy. This refined approach involves the exchange of information among neighboring entities, evaluated against a preset threshold. Through this precise comparison, ECMs strategically reveal their local variables to ensure convergence towards an optimal solution. A detailed case study was conducted to assess the performance, efficiency, and scalability of both methodologies comprehensively. Full article

Compressed sensing (CS) and matrix completion algorithms (MCA) have each introduced sparse and low-rank priors into synthetic aperture radar (SAR) imaging. However, their combined use reveals a fundamental zero-sum trade-off: enhancing spatial continuity tends to obscure weak targets, while strengthening sparse recovery amplifies off-grid artifacts. This inherent conflict is further exacerbated by static regularization, which imposes a rigid global compromise and prevents genuine synergy between the two priors. To overcome this limitation, this paper proposes a Spatio-Temporal Adaptive Synergistic Constraint Imaging (STASCI) algorithm, which dynamically balances the two priors in a scene-aware manner. The core of STASCI is a unified regularization framework. The low-rank constraint models’ spatial continuity in the background to suppress off-grid artifacts. The sparse constraint, enhanced by a non-convex Geman-McClure function, is employed to detect weak targets and compensate for detail loss. A key innovation is a spatio-temporal dual-dimensional regularization mechanism that employs Sobel operators to probe local spatial gradients and dynamically adjusts the strength of each prior according to regional scene characteristics. This enables adaptive synergy rather than a fixed trade-off. The optimization is solved via the alternating direction method of multipliers (ADMM), with the low-rank subproblem accelerated by randomized singular value decomposition (RSVD). Final imaging is performed using the Range Migration Algorithm (RMA). Experiments on real measurements and public datasets demonstrate that STASCI breaks the conventional detail-background trade-off. It effectively suppresses off-grid artifacts while retaining weak targets, leading to significant improvements in imaging accuracy and robustness across complex scenarios. Full article

Phase unwrapping, the recovery of a continuous signal from measurements known only modulo $2\pi$, is a ubiquitous problem in coherent imaging, from medical MRI to radar remote sensing. In Interferometric Synthetic Aperture Radar (InSAR), phase unwrapping is both critical and computationally demanding: current methods require minutes to hours per interferogram and frequently fail on large images. We present FAUST-ADMM (Fast ADMM Unwrapping via Spectral Transforms), an algorithm that formulates phase unwrapping as a weighted $L_1$ optimization and solves it efficiently on GPU using the Alternating Direction Method of Multipliers (ADMM). Each iteration reduces to a Poisson equation solved in closed form via the Discrete Cosine Transform, followed by element-wise soft thresholding, both trivially parallel. On 500 synthetic earthquake interferograms, FAUST-ADMM achieves 99% accuracy with reference-point correction, matching SNAPHU, MCF, and PUMA, while running 10 to 100× faster. On a full three-subswath Sentinel-1 interferogram of the 2019 Ridgecrest M7.1 earthquake (∼6500 × 8500 pixels), FAUST-ADMM agrees with SNAPHU on 99.7% of pixels in 35 s, a $74\times$ speedup. Our method makes batch unwrapping of large InSAR time series practical on a single consumer GPU. Full article

This paper presents a distributed coordinated clearing strategy to facilitate the procurement of energy and flexibility in transmission and distribution networks. This strategy targets 15-min flexibility requirements of energy systems. Inspired by European market practices, a two-stage distributed clearing framework coordinated the transmission system operator (TSO) with distribution system operators (DSOs) is established. TSO and DSOs are responsible for transmission-level and distribution-level energy and flexibility markets, respectively. In the proposed framework, the energy and flexibility markets operate in a coordinated manner and are cleared sequentially, thereby optimizing flexibility procurement for both the transmission network (TN) and active distribution networks (ADNs) to meet the 15-min flexibility requirements of the power system. The alternating direction method of multipliers (ADMM) is used to solve the proposed distributed model while protecting the privacy of all stakeholders. Numerical simulations on a revised IEEE 30-bus transmission with two 33-node ADNs demonstrate that the proposed strategy improves system flexibility provision while enhancing the economic performance of both the TSO and DSOs. Specifically, compared with the decoupled transmission–distribution operation mode, the proposed method can not only reduce the TSO’s flexibility procurement cost by 22.4% but also increase the profits of DSOs by $3066.5. Full article

Post-stack seismic inversion can reconstruct high-resolution acoustic impedance (AI) models from band-limited and noisy seismic reflections, which is crucial for identifying underground structures and characteristics. Traditional regularization methods, including total variation (TV) and total generalized variation (TGV), are prone to oversmoothing and staircase artifacts, thereby limiting their effectiveness in complex geological environments. In this paper, we introduce a novel regularization method based on non-convex TGV (NCTGV), which integrates the classical TGV regularization into a convex non-convex framework. This integration enables the model to simultaneously promote sparsity and preserve higher-order structural continuity. The resulting seismic inversion model was effectively solved using the alternating direction method of multipliers (ADMM), with a provably convergent scheme adapted to the NCTGV structure. Numerical experiments demonstrated the improved performance of the proposed technique. Compared to existing regularization techniques such as TV, NCTV, and TGV, the NCTGV method achieved lower root-mean-square error (RMSE). It also obtained higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) scores, together with enhanced vertical resolution. Visual inspection confirmed that the NCTGV-inverted impedance models exhibited clearer stratigraphic boundaries and sharper geological features. Full article

Microgrids (MGs) have lately received significant attention from researchers as a contemporary solution to better employ the high penetration of renewable energy sources (RESs) to enhance energy sustainability. They can improve the reliability, resilience, and security of distribution systems. However, a distributed energy management framework is required for the optimal operation of distribution systems with multiple microgrids, given the limited communication between the distribution system operator (DSO) and the microgrid operators. Moreover, distribution systems are unbalanced in nature due to the unbalanced connected loads. Thus, modeling the unbalanced power flow in distributed energy management is essential to ensuring the feasibility of operational decisions. This paper proposes a distributed algorithm based on the alternating direction method of multipliers (ADMM) for optimal operation of distribution systems with multi-microgrids, accounting for uncertainty in demand, RESs, and MG operation modes, as well as unbalanced power flow. A modified IEEE 34-bus distribution system with six microgrids is used to validate the effectiveness of the proposed method. The proposed distributed energy management framework can achieve high solution accuracy with limited information shared among operators, as demonstrated in the case study, providing results comparable to those of the centralized energy management approach, with an insignificant 0.24% error in total operating cost. Moreover, numerical results show that compared with the distribution system and microgrids with forecasted loads and PV outputs under normal operation, the proposed stochastic model yields a 0.56% higher total expected operating cost due to uncertainty in load and PV power outputs. When probabilistic MG islanding operation is considered, the total expected operating cost of the distribution system decreases by 1.03% compared with the stochastic solution under normal operation due to the microgrids’ disconnection from the distribution system during islanding in a few scenarios, hence relieving the distribution system of excessive load. Full article

To address the issues of texture residuals and structural detail loss often encountered in traditional image decomposition methods, this paper proposes an unsupervised decomposition model that integrates the Wavelet Transform with Deep Image Prior (DIP). Leveraging the multi-scale and multi-directional characteristics of the Wavelet Transform, the model carefully models the structural information of the cartoon component. Meanwhile, capitalizing on the unsupervised learning advantages of Deep Image Prior and incorporating low-rank constraints, it accurately extracts texture details. The model is solved via the Alternating Direction Method of Multipliers (ADMM). Experimental results on multiple test images demonstrate that, compared with existing methods, the proposed model achieves a more thorough separation of image structure and texture, yielding high-quality visual decomposition performance. Full article

To address the challenges of power fluctuations caused by the integration of distributed generation (DG) and the difficulty in simultaneously managing peak-valley load regulation due to diverse user energy demands in a microgrid system, this paper presents a coordinated optimal configuration method for serving a hybrid energy storage system (HESS), which explicitly considers the differentiated requirements from both the supply-side and the demand-side. In the presented method, an improved empirical mode decomposition (EMD) method is first presented to decompose the DG power into high-frequency, medium-frequency, and low-frequency bands. Based on the complementary technical and economic characteristics of different energy storage types, a coordinated regulation strategy for HESS in the multiple time-frequency domains is developed. Second, a coordinated optimal configuration model for HESS is further established. This model integrates key performance indicators, including maximum fluctuation and renewable energy utilization rate on the supply-side and the peak-valley difference reduction rate on the demand-side. Finally, a distributed optimization algorithm based on an improved alternating direction method of multipliers (ADMM) is developed to solve the coordinated configuration model. The experimental results demonstrate that the presented method can effectively smooth the DG power fluctuations and reduce the load peak-valley difference. The renewable energy utilization rate reaches 100%, and the peak-valley difference reduction rate reaches approximately 80%. The presented method successfully achieves the coordinated optimal configuration of HESS on both the supply and demand sides, providing a theoretical underlying infrastructure for the configuration of energy storage in the microgrid system with high penetration of renewable energy. Full article

Being capable of high-resolution imaging and non-contact measurement, Ground Penetrating Radar (GPR) is a promising technology for the detection of unexploded ordnance (UXO). However, UXO detection is severely hindered by clutter, particularly in environments with significant surface roughness where conventional suppression methods prove ineffective. To address this, we propose a polarimetric GPR clutter suppression method based on an improved non-convex Tensor Robust Principal Component Analysis (TRPCA) framework. Specifically, a polarization-aware tensor construction scheme is designed by stacking the HH and VV channel data. This approach exploits the strong inter-channel correlation of clutter to enhance its low-rank property, while highlighting the distinct sparse signatures of targets derived from their polarimetric responses. To further optimize tensor decomposition, we introduce a non-convex Tensor Adjustable Logarithmic Norm (TALN) to overcome the estimation bias inherent in the conventional Tensor Nuclear Norm (TNN). Serving as a tighter surrogate for tensor rank, the proposed TALN regularizer improves the approximation accuracy of the low-rank component, thereby ensuring a clearer separation between clutter and targets. The resulting non-convex optimization problem is efficiently solved using Alternating Direction Method of Multipliers (ADMM). Numerical simulations and laboratory experiments demonstrate that the proposed method suppresses strong clutter stemming from rough-surface reflections more effectively than existing methods, achieving a Signal-to-Clutter Ratio (SCR) improvement of over 20 dB. Full article

In prestack seismic inversion for structurally complex areas, elastic parameters commonly show strong directional heterogeneity (layer-parallel continuity versus cross-layer discontinuities), so conventional structure-guided schemes based on isotropic regularization often struggle to achieve both numerical stability and sharp interface resolution. To address this issue, we develop a structure-oriented, direction-decomposed Lp-L2 regularization method for prestack multi-trace joint inversion of P-wave velocity (Vp), S-wave velocity (Vs), and density (ρ). Dip information extracted from poststack seismic data is used to construct a dip-guided directional operator that locally projects the Cartesian model-gradient field onto the tangential and normal directions of the structural field, corresponding to along-layer and cross-layer components, respectively. Different priors are then imposed: L2 smoothing along layers enhances lateral continuity and stabilizes the inversion, whereas a nonconvex Lp sparsity constraint across layers concentrates updates at a limited number of geological discontinuities and preserves sharp contrasts at faults and bed boundaries, thereby mitigating the over-smoothing typical of dip-guided L2 inversion. The resulting formulation is embedded in a linearized prestack Amplitude Versus Offset (AVO) framework and solved efficiently using the Alternating Direction Method of Multipliers (ADMM) algorithm. Synthetic tests and field-data applications demonstrate improved delineation of faults and thin-bed boundaries under noise, with reduced errors and higher correlation relative to a classical structure-guided L2 approach. These results indicate that the proposed method provides a practical and effective route for high-resolution prestack elastic-parameter characterization in complex tectonic settings. Full article

The sustainable collaborative operation of multi-park integrated energy systems (MPIESs) with shared energy storage (SES) provides a significant pathway for low-carbon transition, renewable energy utilization, and energy efficiency improvement, thereby supporting regional energy sustainability. However, realizing this potential faces challenges, including source-load uncertainty, conflicts of interest among multiple entities, and the need for privacy-preserving distributed coordination. To address these issues, this paper proposes a distributed robust energy management strategy for MPIESs with SES, which is decomposed into two sub-problems. In the first sub-problem, a robust optimization model incorporating the SES leasing mechanism is established to handle the uncertainties of photovoltaic (PV) generation and loads. In the second sub-problem, a cooperative game model based on Nash bargaining theory is constructed to fairly allocate the cooperative surplus among participating parks. The alternating direction method of multipliers (ADMM) is employed to solve the overall model in a distributed manner, and enabling collaborative scheduling with limited information exchange. Case studies indicate that the proposed strategy reduces the total system operating cost by 17.57% compared to the independent operation mode. The benefit allocation mechanism achieves Pareto improvement and effectively mitigates the uneven distribution of cooperative surplus among parks. Furthermore, the distributed algorithm converges within 13 iterations in the test case, demonstrating good computational tractability. Consequently, the results verify the effectiveness of the proposed framework in balancing economy, fairness, and robustness, thereby promoting the low-carbon and sustainable operation of regional integrated energy systems. Full article

Multimodal medical image fusion provides vital anatomical and pathological details for clinical diagnosis. However, existing diffusion algorithms often struggle with prolonged inference times and local structure loss. To address these critical issues in applied medical imaging, we propose an accelerated edge-aware diffusion model with spatial refinement. This framework utilizes a coarse-to-fine collaborative architecture. It first extracts structural priors via edge-enhanced data blocks and a non-uniform time-step accelerated sampling strategy. During refinement, a spatially adaptive non-convex variational module employs a Nesterov accelerated alternating direction method of multipliers for pixel-level correction to efficiently remove diffusion artifacts and sharpen anatomical boundaries. We conduct extensive comparative experiments against the vanilla diffusion baseline and state-of-the-art deep learning paradigms. Qualitative and quantitative evaluations on clinical datasets demonstrate the superior balanced performance of our model. The framework delivers highly natural visual representations, effectively merging sharp skeletal contours from computed tomography with rich soft tissue textures from magnetic resonance imaging while preventing unnatural over-sharpening. Additionally, it demonstrates outstanding performance across comprehensive statistical metrics, reflecting exceptional image fidelity, robust global contrast, and precise structural preservation. Furthermore, the model reduces inference time by approximately 42% compared to the baseline. Ultimately, this framework strikes an optimal balance between superior image fusion quality and computational efficiency, offering enhanced visual representations with potential utility for clinical image processing under limited resources. Full article

Polarimetric high-resolution range profiles (HRRPs) contain rich amplitude and phase information scattered from targets, making them essential for radar remote sensing applications. However, current HRRP imaging methods still face challenges in achieving precise full-polarization measurements. In addition, they are either affected by off-grid errors thus introducing spurious scattering centers (SCs), fail to utilize polarimetric priors from the channels, or encounter high computational complexity. Some of these issues limit the quality of polarimetric HRRPs, while others result in excessive computational load, hindering their application on orbital remote sensing platforms. This paper proposes a fast gridless polarimetric HRRP imaging method. First, we introduce the novel virtual full polarization sparse stepped-frequency waveforms (VFP-SSFW) to improve channel isolation, in which each pulse is transmitted with either horizontal (H) or vertical (V) polarization, selected uniformly at random. Then, we propose a polarimetric atomic norm minimization (P-ANM)-based imaging framework formulated within distributed compressed sensing (DCS), which fully exploits the joint sparsity across polarization channels while inherently eliminating off-grid errors. Additionally, we develop a fast algorithm based on alternating direction method of multipliers (ADMM) to enable efficient implementation. The proposed method can circumvent transmission channel crosstalk and can efficiently yield high-quality polarimetric HRRPs with co-registered SCs. The validity of the proposed method is demonstrated through simulated, electromagnetic, and measured experimental results. Full article

Deep learning (DL) methods have achieved promising performance in infrared weak target segmentation. However, their interpretability and robustness against cluttered backgrounds and noise remain limited. We propose an adaptive-weighted deep unfolding network (AwDuNet) that unfolds alternating direction method of multipliers (ADMM) iterations for adaptive sparse–low-rank decomposition into multi-stage interpretable modules for end-to-end training. An adaptive weight matrix is jointly estimated from a local structural-difference matrix and a sparse-enhancement matrix, thereby strengthening target–background separation while preserving fine target details. To suppress background clutter, we design a dual-path complementary attention (DCA) mechanism for the low-rank background reconstruction module (LBRM), which improves low-rank background modeling by jointly leveraging spatial and channel attention. By extracting local details and global context in parallel, DCA enhances weak-target responses and mitigates interference from complex backgrounds. We also build a real-scene infrared dataset with 632 images for out-of-domain evaluation. The model is tested without fine-tuning after training on public datasets to assess practical robustness. Experiments on multiple public datasets validate the effectiveness and generalization of AwDuNet. Full article