Current dark image restoration methods suffer from severe efficiency bottlenecks, primarily stemming from: computational burden and error correction costs associated with reliance on external priors (manual or cross-modal); redundant operations in complex multi-stage enhancement pipelines; and indiscriminate processing across frequency components in frequency-domain methods, leading to excessive global computational demands. To address these challenges, we propose an Efficient Self-Mining Prior-Guided Joint Frequency Enhancement Network (SPJFNet). Specifically, we first introduce a Self-Mining Guidance Module (SMGM) that generates lightweight endogenous guidance directly from the network, eliminating dependence on external priors and thereby bypassing error correction overhead while improving inference speed. Second, through meticulous analysis of different frequency domain characteristics, we reconstruct and compress multi-level operation chains into a single efficient operation via lossless wavelet decomposition and joint Fourier-based advantageous frequency enhancement, significantly reducing parameters. Building upon this foundation, we propose a Dual-Frequency Guidance Framework (DFGF) that strategically deploys specialized high/low frequency branches (wavelet-domain high-frequency enhancement and Fourier-domain low-frequency restoration), decoupling frequency processing to substantially reduce computational complexity. Rigorous evaluation across multiple benchmarks demonstrates that SPJFNet not only surpasses state-of-the-art performance but also achieves significant efficiency improvements, substantially reducing model complexity and computational overhead.

Recovering fine-grained details in extremely dark images remains challenging due to severe structural information loss and noise corruption. Existing enhancement methods often fail to preserve intricate details and sharp edges, limiting their effectiveness in downstream applications like text and edge detection. To address these deficiencies, we propose an efficient dual-stage approach centered on detail recovery for dark images. In the first stage, we introduce a Residual Fourier-Guided Module (RFGM) that effectively restores global illumination in the frequency domain. RFGM captures inter-stage and inter-channel dependencies through residual connections, providing robust priors for high-fidelity frequency processing while mitigating error accumulation risks from unreliable priors. The second stage employs complementary Mamba modules specifically designed for textural structure refinement: (1) Patch Mamba operates on channel-concatenated non-downsampled patches, meticulously modeling pixel-level correlations to enhance fine-grained details without resolution loss. (2) Grad Mamba explicitly focuses on high-gradient regions, alleviating state decay in state space models and prioritizing reconstruction of sharp edges and boundaries. Extensive experiments on multiple benchmark datasets and downstream applications demonstrate that our method significantly improves detail recovery performance while maintaining efficiency. Crucially, the proposed modules are lightweight and can be seamlessly integrated into existing Fourier-based frameworks with minimal computational overhead.

Urban region profiling, the task of characterizing geographical areas, is crucial for urban planning and resource allocation. However, existing research in this domain faces two significant limitations. First, most methods are confined to single-task prediction, failing to capture the interconnected, multi-faceted nature of urban environments where numerous indicators are deeply correlated. Second, the field lacks a standardized experimental benchmark, which severely impedes fair comparison and reproducible progress. To address these challenges, we first establish a comprehensive benchmark for multi-task urban region profiling, featuring multi-modal features and a diverse set of strong baselines to ensure a fair and rigorous evaluation environment. Concurrently, we propose UrbanMoE, the first sparse multi-modal, multi-expert framework specifically architected to solve the multi-task challenge. Leveraging a sparse Mixture-of-Experts architecture, it dynamically routes multi-modal features to specialized sub-networks, enabling the simultaneous prediction of diverse urban indicators. We conduct extensive experiments on three real-world datasets within our benchmark, where UrbanMoE consistently demonstrates superior performance over all baselines. Further in-depth analysis validates the efficacy and efficiency of our approach, setting a new state-of-the-art and providing the community with a valuable tool for future research in urban analytics.

Traditional Low-Light Image Enhancement (LLIE) methods primarily focus on uniform brightness adjustment, often neglecting instance-level semantic information and the inherent characteristics of different features. To address these limitations, we propose CWNet (Causal Wavelet Network), a novel architecture that leverages wavelet transforms for causal reasoning. Specifically, our approach comprises two key components: 1) Inspired by the concept of intervention in causality, we adopt a causal reasoning perspective to reveal the underlying causal relationships in low-light enhancement. From a global perspective, we employ a metric learning strategy to ensure causal embeddings adhere to causal principles, separating them from non-causal confounding factors while focusing on the invariance of causal factors. At the local level, we introduce an instance-level CLIP semantic loss to precisely maintain causal factor consistency. 2) Based on our causal analysis, we present a wavelet transform-based backbone network that effectively optimizes the recovery of frequency information, ensuring precise enhancement tailored to the specific attributes of wavelet transforms. Extensive experiments demonstrate that CWNet significantly outperforms current state-of-the-art methods across multiple datasets, showcasing its robust performance across diverse scenes. Code is available at https://github.com/bywlzts/CWNet-Causal-Wavelet-Network.

In recent years, infrared small target detection (ISTD) networks based on deep learning have achieved notable advances. However, these methods still face significant challenges when applied to the real world. Most of them lack the fundamental principles of small target detection in infrared imagery, which results in difficulties in distinguishing targets from complex backgrounds and poor interpretability. To address these challenges, an interpretable network architecture for ISTD, termed low-rank sparse decomposition state space models (LSDSSMs), is proposed. LSDSSMs use the principles of low-rank and sparse decomposition, incorporating dedicated modules for the low-rank space separation module and the sparse target extraction module. These modules facilitate the extraction of sparse representations for both low-rank backgrounds and small targets. In addition, a joint reconstruction module is employed to integrate these components, generating reconstructed images. Considering the unique imaging characteristics of infrared images and the sparse nature of small targets, a channel selection module (CSM) is proposed to enhance the extraction of sparse targets. To enhance the adaptability, stability, and resistance resistance of LSDSSMs in complex environments, robust state space models are integrated that combine local and global information representations. Furthermore, a multilevel loss function is introduced to enforce comprehensive constraints on low-rank backgrounds, sparse targets, and reconstructed images. This design improves not only the robustness of the LSDSSMs but also its performance across different scenarios. Extensive experimental results demonstrate that LSDSSMs surpass existing baseline methods in both qualitative and quantitative assessments, validating their effectiveness and reliability.

Edge computing is considered as a promising paradigm to support vehicular applications in the upcoming sixth-generation (6G) vehicular networks. In the context of vehicular edge computing (VEC), the self-interested vehicular users and edge servers work towards incongruous goals. Such mixed-motive setting is detrimental to the collective good, sometimes leading to social dilemmas. To resolve such a conflict, we first formulate a bi-level optimization problem to model mixed-motive task offloading. In this case, vehicular users aim to improve energy efficiency under strict low-latency requirements, whereas edge servers attempt to increase serving efficiency. To address it, we propose a scheme based on bi-level reinforcement learning, i.e., bi-level multi-agent actor-critic (BLMAAC) framework. Specifically, upper-level edge servers make iterative optimization under the best responses of lower-level vehicular users, which can be regarded as a Stackelberg game. Theoretically, we identify the conditions and prove the convergence of the framework that is able to reach Stackelberg equilibrium strategy. By numerical evaluation, the high-utilization edge servers and energy-efficient vehicular users demonstrate the superiority of the bi-level structure. Moreover, the proposed scheme outperforms other actor-critic based learning algorithms and two-stage methods exploring Nash equilibrium strategy.

Abstract Attention mechanisms are popular techniques in computer vision that mimic the ability of the human visual system to analyse complex scenes, enhancing the performance of convolutional neural networks (CNN). In this paper, the authors propose a refined global attention module (RGAM) to address known shortcomings of existing attention mechanisms: (1) Traditional channel attention mechanisms are not refined enough when concentrating features, which may lead to overlooking important information. (2) The 1‐dimensional attention map generated by traditional spatial attention mechanisms make it difficult to accurately summarise the weights of all channels in the original feature map at the same position. The RGAM is composed of two parts: refined channel attention and refined spatial attention. In the channel attention part, the authors used multiple weight‐shared dilated convolutions with varying dilation rates to perceive features with different receptive fields at the feature compression stage. The authors also combined dilated convolutions with depth‐wise convolution to reduce the number of parameters. In the spatial attention part, the authors grouped the feature maps and calculated the attention for each group independently, allowing for a more accurate assessment of each spatial position’s importance. Specifically, the authors calculated the attention weights separately for the width and height directions, similar to SENet, to obtain more refined attention weights. To validate the effectiveness and generality of the proposed method, the authors conducted extensive experiments on four distinct medical image segmentation datasets. The results demonstrate the effectiveness of RGAM in achieving state‐of‐the‐art performance compared to existing methods.

Abstract Intracranial haemorrhage (ICH) is a haemorrhagic disease that occurs in the ventricle or brain tissue and has a high probability of mortality and disability. For ICH, it is important to obtain a correct diagnosis in the early stages. Currently, ICH classification mainly depends on professional radiologists for manual diagnosis. Therefore, it is necessary to develop a method that can efficiently and rapidly diagnose ICH. In the field of ICH subtype classification, most studies directly use the existing convolutional neural network (CNN) to extract CT slice features. However, these existing networks have the following shortcomings: (1) insufficient discrimination of CT slice features leads to an inability to achieve satisfactory classification performance. (2) Most CT slice data sets of ICH have the serious problem of sample imbalance. (3) There is a correlation between subtypes; however, in previous studies, this correlation has been ignored. To solve these problems, the authors propose a classification algorithm for ICH subtypes applied to CT images. The CNN–RNN architecture was adopted to classify ICH subtypes. In the CNN module, the problem is viewed from a fine‐grained perspective, which solves the problem of insufficient feature discrimination in existing methods. A new loss function is also proposed to solve the problems of unbalanced data distribution and neglected dependencies among the labels. These parts are integrated into the proposed fine‐grained network architecture. The image embeddings were obtained by the CNN module and then input to the RNN module. The authors’ method was evaluated on the Radiological Society of North America 2019 Brain CT Haemorrhage (RSNA‐2019) benchmark. The experimental results demonstrated that the performance of the proposed method is state‐of‐the‐art.

Spectrum sharing in cellular vehicle-to-everything (C-V2X) has been conceived as a promising solution to improve spectrum efficiency. However, the co-channel interference incurred with it may cause severe performance degradation to vehicular links. Thereby, radio resource management (RRM) is motivated and designed to ensure communication reliability and increase system capacity. One challenge is that RRM involves channel allocation and power control, which are tightly coupled and hard to optimize simultaneously. Another challenge for this is the difficulty adapting centralized RRM schemes, requiring global channel state information (CSI) and causing high signaling overhead. To tackle these challenges, we propose the hybrid centralized-distributed RRM scheme and the distributed RRM scheme. Specifically, we prove a decoupling method that provides a theoretical lower bound so that channel allocation and power control can be optimized independently. Given the decoupling method, the hybrid centralized-distributed RRM scheme is based on graph matching and reinforcement learning (GMRL) to maximize system capacity and guarantee reliability requirements. Further, to decrease computation complexity and signaling overhead, the distributed RRM scheme that only requires local CSI with hybrid-framework reinforcement learning (HFRL) is exploited. Finally, both schemes are numerically evaluated through experiments and outperform other deep Q-network (DQN)-based schemes.

Moving ground-target detection system is widely used to monitor illegal activities of pedestrians and vehicles. However, existing detection methods are restricted by the power consumption in hardware and are usually based on some single feature of the seismic signal, which leads to low detection accuracy and false alarms. To address these issues, we propose a new moving ground-target detection method for detecting the weak seismic signals generated by distant moving ground targets. This method combines an adaptive strategy and support vector machines (SVMs). Both time- and frequency-domain features of seismic signals are considered in the detection method. Additionally, we carry out field experiments to evaluate the performance of the proposed method. The results show that the proposed moving ground-target detection method can detect distant moving ground targets and avoid false alarms as many as possible, which indicates good performance.